DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-23 are currently pending and examined on the merits.
Claims 1-23 are rejected.
Claims 10-11 are objected to.
Priority
The instant application claims priority to U.S. Provisional Application 63/208,904 filed on 9 June 2021. At this point in examination, the effective filing date of claims 1-23 is 9 June 2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 30 November 2022 and 8 December 2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because of the following:
Reference character “134” in Figure 1B has been used to designate both “Graph Convolution Block” and “Molecule Graph Data”.
Reference character “190” in Figure 1E has been used to designate “Internal Coordinates L (
b
i
,
a
i
,
d
i
)”, “Transform To Cartesian Coordinates
C
°
”, and “Generated Conformation
C
K
”
Reference character “192” in Figure 1E has been used to designate both “K Iterations (EDG Optimizer)” and “Conformation – Rotation and Translation Invariant Internal Coordinates”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because "Moleule Graph Data" 134 in Figures 1A and 1B is misspelled and should read "Molecule Graph Data". Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The attempt to incorporate subject matter into this application by reference to WO 2012/229454 in paragraph 77, line 5 of the specification is ineffective because the publication could not be found or does not exist. See MPEP 608.01(p).
Claim Objections
Claims 10-11 are objected to because of the following informalities: Claim 10, last line recites "confirmation discriminator". Claim 11, last line recites "predicting distances between neighboring atoms of the first confirmation." There is a typographical error, where "confirmation" is misspelled and should read "conformation". Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-21 are directed to a method (process) that uses molecular graph data for a molecule as input and provides a report that includes at least one selected conformation for the molecule as an output. Claim 22 is directed to one or more non-transitory computer-readable storage media (machine). Claim 23 is directed to a system (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-3, 5-14, and 16-23 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claims 1, 22, and 23 further recite:
generating a plurality of conformations for the molecule with the machine learning platform (i.e., mental processes);
selecting at least one conformation for the molecule based on at least one parameter related to molecular conformations (i.e., mental processes);
preparing a report that includes the selected at least one conformation for the molecule (i.e., mental processes).
Dependent claim 2 further recites:
further comprising the machine learning platform predicting lengths for each molecular graph bond of the molecule for each conformation (i.e., mental processes).
Dependent claim 3 further recites:
providing the at least one selected conformation of the molecule that has a lower energy compared to other generated conformations of the molecule (i.e., mental processes).
Dependent claim 5 further recites:
inputting molecule graph data of the molecule and a set of latent vectors into a generator (i.e., mental processes);
outputting a conformation of the molecule as a sequence of internal coordinates (i.e., mental processes);
distinguishing real conformations from generated conformations with predicted energy differences (i.e., mental processes);
mapping conformations into latent space (i.e., mental processes);
conforming the latent space to be similar to a prior distribution (i.e., mental processes).
Dependent claim 6 further recites:
further comprising a conformation generation (i.e., mental processes);
generating internal coordinates of a first conformation from the molecule graph data and noise (i.e., mental processes);
predicting bond lengths and a bond-wise loss function weight of the first conformation (i.e., mental processes);
converting the internal coordinates to Cartesian coordinates for the first conformation (i.e., mental processes, mathematical concepts);
computing the Cartesian coordinates for unit direction and unit normal vectors for the conformation (i.e., mental processes, mathematical concepts);
modulating bond length of the conformation to the predicted bond lengths (i.e., mental processes).
Dependent claim 7 further recites:
representing the molecular graph by nodes and edge feature sets (i.e., mental processes);
extending the molecular graph with auxiliary nodes and edges to make a proposed generative model (i.e., mental processes);
introducing virtual edges between second, third, and/or fourth neighboring nodes (i.e., mental processes);
setting each node to include a description of: atom type, charge, and chiral tag (i.e., mental processes);
setting each edge feature to include a first graph subset that has chemical bond type and bond stereochemistry (i.e., mental processes);
setting each edge feature to include a second graph subset that has a spanning tree traversal process and having defining edge features to be in the spanning tree and information regarding whether a source node appears earlier in the spanning tree traversal process than a destination node (i.e., mental processes).
Dependent claim 8 further recites:
estimating one or more of the following conformation properties for each generated molecule: asphericity, eccentricity, inertial shape factor, two normalized principal moments ratios, three principal moments of inertia, gyration radius or spherocity index (i.e., mental processes).
Dependent claim 9 further recites:
operating a molecular graph generator to obtain molecular graph data and latent code data to construct a conformation of a molecule with a set of internal coordinates, to convert the internal coordinates into Cartesian coordinates, and perform at least one optimization to correct local distance geometry of at least one molecular substructure (i.e., mental processes, mathematical concepts);
operating a conformation discriminator to distinguish between real conformations of a molecule from synthetic conformations of the molecule (i.e., mental processes);
operating a latent variables discriminator to map conformations into the latent space and to make the latent space similar to a normal prior distribution (i.e., mental processes).
Dependent claim 10 further recites:
determining a reconstruction loss between an original conformation of a molecule compared to a reconstructed conformation of the molecule by adversarial analysis between the molecular graph generator against the confirmation discriminator and latent variables discriminator (i.e., mental processes, mathematical concepts).
Dependent claim 11 further recites:
constructing a first conformation having a rotation and translation invariant representation (i.e., mental processes);
predicting distances between neighboring atoms of the first confirmation (i.e., mental processes)
Dependent claim 12 further recites:
considering a potential energy of a plurality of conformations (i.e., mental processes);
selecting physically plausible conformations based on the potential energy of each selected conformation (i.e., mental processes).
Dependent claim 13 further recites:
modeling at least one provided conformation of the molecule with a biological target (i.e., mental processes);
determine whether or not the at least one provided conformation modulates the biological target (i.e., mental processes).
Dependent claim 14 further recites:
operating a graph convolution block (i.e., mental processes);
update representations of nodes and edges of a molecule graph data (i.e., mental processes);
update node states (i.e., mental processes);
update hidden states of edges (i.e., mental processes).
Dependent claim 16 further recites:
encoding discrete features of nodes and edge features with embedding layers, each edge feature including a first graph subset that has a chemical bond type and bond stereochemistry (i.e., mental processes);
applying a sequence of graph convolution blocks to the discrete features to obtain an embedding of the molecular graph of the molecule (i.e., mental processes).
Dependent claim 17 further recites:
further comprising an encoder: obtaining a description of a conformation from molecular graph data of a molecule (i.e., mental processes);
further comprising an encoder: transforming the conformation with a sequence of graph convolution blocks to obtain node-wise latent codes (i.e., mental processes).
Dependent claim 18 further recites:
further comprising a latent variables discriminator: distinguishing generated latent codes of real conformations from noise (i.e., mental processes);
further comprising a latent variables discriminator: determining: node-wise latent codes being independent of each other; and node-wise latent codes following the normal distribution (i.e., mental processes).
Dependent claim 19 further recites:
further comprising a conformation discriminator: controlling quality of generated objects by: assessing a likelihood of one or more conformations (i.e., mental processes);
further comprising a conformation discriminator: controlling quality of generated objects by: determining a quality of the one or more conformations based on potential energy estimations (i.e., mental processes).
Dependent claim 20 further recites:
further comprising a conformation discriminator: obtaining one aggregated value for the whole molecular conformation (i.e., mental processes, mathematical concepts).
Dependent claim 21 further recites:
determining an ability to synthesize generated molecular conformation (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-3, 5-14, and 16-23 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 2-3, 5-8, 10-14, 16-19, and 21 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Claim 1 recites inputting the molecule graph data into a machine learning platform. The machine learning platform is obtaining data for further analysis, which is considered a well-understood, routine, and conventional activity. Data gathering steps are extra-solution activity as they collect the data needed to carry out the JE. It does not impose any meaningful limitation on the JE or how the JE is performed (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claim 1 recites obtaining molecule graph data for a molecule. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claim 9 recites operating a stochastic encoder to construct an irredundant latent space of latent data of input molecules and prevent mode collapse. The limitation recites using a stochastic encoder, which provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claim 15 recites inputting condition data into the machine learning platform. The machine learning platform is obtaining data for further analysis, which is considered a well-understood, routine, and conventional activity. Data gathering steps are extra-solution activity as they collect the data needed to carry out the JE. It does not impose any meaningful limitation on the JE or how the JE is performed (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claim 20 recites passing molecular graph embeddings through a plurality of SchNet layers to obtain node representations. The limitation of passing molecular graph embeddings through SchNet layers provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claims 22 and 23 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
One or more non-transitory computer readable media storing instructions (claim 22);
A computer system comprising: one or more processors; and one or more non-transitory computer readable media storing instructions (claim 23).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-23 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 9, 15, 20, and 22-23 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to the recited obtaining molecule graph data for a molecule (claim 1): The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
The additional element of inputting the molecule graph data into a machine learning platform (claim 1) is conventional. Evidence for conventionality is shown by Ganea et al. (NeurIPS, 2021, 1-13), as provided in the IDS filed 11/30/2022. Ganea et al. reviews “We propose GeoMol – an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers.” (Abstract, lines 6-8). Also, further reviews “We generate a representative set of low-energy 3D conformers from the input molecular graph.” (Figure 1). This shows that GeoMol is the machine learning platform that takes in molecule graph data, which makes it a conventional element in the art.
The additional element of operating a stochastic encoder to construct an irredundant latent space of latent data of input molecules and prevent mode collapse (claim 9) is conventional. Evidence for conventionality is shown by Winter et al. (CoRR, 2021, 1-6), as provided in the IDS filed 11/30/2022. Winter et al. reviews “The overall goal of the proposed model is to find functions
f
Θ
and
g
Θ
that map a conformation
Ξ
G
of a molecule
G
to and from a fixed-sized latent representation
z
Ξ
∈
R
F
z
, respectively.” (Page 2, Section “2.2 Conformation Autoencoder”, lines 1-2). Also, further reviews “by training a probabilistic model on a large dataset of molecular conformations, we demonstrate how our model can be used to generate diverse sets of energetically favorable conformations for a given molecule.” (Abstract, lines 5-8). This shows that an encoder model is used to construct an irredundant latent space of latent representation data and prevents mode collapse because the model generates diverse sets of conformations. Therefore, operating a stochastic encoder is a conventional element in the art.
The additional element of inputting condition data into the machine learning platform (claim 15) is conventional. Evidence for conventionality is shown by Ganea et al. (NeurIPS, 2021, 1-13), as provided in the IDS filed 11/30/2022. Ganea et al. reviews “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc. For each molecular graph G, we have a variable-size set of low-energy ground truth 3D conformers
{
C
l
*
}
l
that we predict with a model
{
C
k
}
k
≝
ζ
(
G
)
.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This shows that the machine learning platform takes in condition data, where the condition data includes at least one conformation of the molecule. Therefore, inputting condition data into a machine learning platform is a conventional element in the art.
The additional element of passing molecular graph embeddings through a plurality of SchNet layers to obtain node representations (claim 20) is conventional. Evidence for conventionality is shown by Schutt et al. (The Journal of Chemical Physics, 2018, 148(24), 1-11), as provided in the IDS filed 11/30/2022. Schutt et al. reviews “SchNet is a variant of the earlier proposed Deep Tensor Neural Network (DTNN) and therefore shares a number of their essential building blocks. Among these are atom embeddings interaction refinements and atom-wise energy contributions. At each layer, the atomistic system is represented atom-wise being refined using pairwise interactions with the surrounding atoms. In the DTNN framework, interactions are modeled by tensor layers, i.e., atom representations and interatomic distances are combined using a parameter tensor.” (Page 2, Section “II. Method”, lines 1-10). This shows that molecular graph embeddings are refined by passing through SchNet layers to obtain node or atom representations, making it a conventional element in the art.
With respect to claims 22 and 23: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-23 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 11-13, 15-16, 19, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13), as provided in the IDS filed 11/30/2022.
With respect to claims 1, 22, and 23:
With respect to the recited obtaining molecule graph data for a molecule, Ganea et al. discloses a “molecular graph” (Figure 1). This figure suggests that molecule graph data of a molecule was obtained.
With respect to the recited inputting the molecule graph data into a machine learning platform, Ganea et al. discloses “We propose GeoMol – an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers.” (Abstract, lines 6-8). This suggests that GeoMol is the machine learning platform that uses molecule graph data to generate molecular conformers.
With respect to the recited generating a plurality of conformations for the molecule with the machine learning platform, wherein the plurality conformations are specific to the molecule, each conformation having internal coordinates defining positions of atoms of the molecule, Ganea et al. discloses “We tackle the problem of molecular conformer generation (MCG), i.e. predicting the ensemble of low-energy 3D conformations of a small molecule solely based on the molecular graph (fig. 1). A single conformation is represented by the list of 3D coordinates for each atom in the respective molecule.” (Page 1, Section “Problem & importance”, paragraph 1, lines 1-7). This suggests that the ensemble of low-energy 3D conformations specific to a small molecule is generated with GeoMol and each conformation is represented by coordinates defining positions of atoms of the molecule.
With respect to the recited selecting at least one conformation for the molecule based on at least one parameter related to molecular conformations, Ganea et al. discloses “In this work, we assume that the low-energy states are implicitly defined by the given dataset, i.e., our training data consist of molecular graphs and corresponding sets of energetically favorable 3D conformations. Low-energy structures are the most stable configurations and, thus, expected to be observed most often experimentally.” (Page 1, Section “Problem & importance”, paragraph 1, lines 7-12). This suggests that the conformations for the molecule are selected based on low-energy states, which is a parameter related to molecular conformations.
With respect to the recited preparing a report that includes the selected at least one conformation for the molecule, Ganea et al. discloses “We propose GeoMol – an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers.” (Abstract, lines 6-8). The distributions of molecular 3D conformers generated are the conformations selected for a report.
With respect to claim 2:
With respect to the recited further comprising the machine learning platform predicting lengths for each molecular graph bond of the molecule for each conformation, Ganea et al. discloses “It explicitly models and predicts essential molecular geometry elements: torsion angles and local 3D structures (bond distances and bond angles adjacent to each atom).” (Page 3, Section “Our key contributions & model in a nutshell”, third bullet, lines 1-2). This suggests predicting distances for each molecular graph bond of the molecule for each conformation.
With respect to claim 3:
With respect to the recited wherein the at least one parameter related to molecular conformations includes an energy of each conformation, the method comprising providing the at least one selected conformation of the molecule that has a lower energy compared to other generated conformations of the molecule, Ganea et al. discloses “In this work, we assume that the low-energy states are implicitly defined by the given dataset, i.e., our training data consist of molecular graphs and corresponding sets of energetically favorable 3D conformations. Low-energy structures are the most stable configurations and, thus, expected to be observed most often experimentally.” (Page 1, Section “Problem & importance”, paragraph 1, lines 7-12). This suggests that the conformations selected are based on low-energy states and only low energy structures are considered compared to other generated conformations.
With respect to claim 4:
With respect to the recited the report including a conformation space that is comprised of a plurality of overlaid selected conformations for the molecule, Ganea et al. discloses “We generate a representative set of low-energy 3D conformers from the input molecular graph. This example molecule has both rigid (rings) and flexible parts. Conformers are shown aligned and juxtaposed.” (Figure 1). The report includes a representative set of selected conformations generated in a conformation space as seen in Figure 1.
With respect to claim 11:
With respect to the recited constructing a first conformation having a rotation and translation invariant representation, Ganea et al. discloses “It models conformers in an SE(3)-invariant (translation/rotation) manner by design.” (Page 3, Section “Our key contributions & model in a nutshell”, second bullet, line 1). This suggests modeling a conformation having a rotation and translation invariant representation.
With respect to the recited predicting distances between neighboring atoms of the first confirmation, Ganea et al. discloses “It explicitly models and predicts essential molecular geometry elements: torsion angles and local 3D structures (bond distances and bond angles adjacent to each atom).” (Page 3, Section “Our key contributions & model in a nutshell”, third bullet, lines 1-2). This suggests predicting bond distances between atoms of a conformation, which is an essential molecular geometry element.
With respect to claim 12:
With respect to the recited considering a potential energy of a plurality of conformations, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). This suggests that potential energy of a plurality of generated conformers is considered.
With respect to the recited selecting physically plausible conformations based on the potential energy of each selected conformation, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). Also, further discloses “The energy values from GeoMol are the lowest among the ML methods, indicating greater stability of generated conformers, especially for the druglike molecules.” (Supplementary Information, Page 20, Appendix K Energy calculations, lines 1-3). This suggests that generated conformers are selected based on the potential energy values of each conformation.
With respect to claim 13:
With respect to the recited modeling at least one provided conformation of the molecule with a biological target, Ganea et al. discloses “We expect that such differentiable structure generators will significantly impact small molecule conformer generation along with many related applications (e.g., protein-ligand binding), thus speeding up areas such as drug discovery.” (Page 10, Section “Conclusion”, lines 2-5). This implies that GeoMol can assist in modeling a conformation with a biological target for applications such as protein-ligand binding.
With respect to the recited determine whether or not the at least one provided conformation modulates the biological target, Ganea et al. discloses “We expect that such differentiable structure generators will significantly impact small molecule conformer generation along with many related applications (e.g., protein-ligand binding), thus speeding up areas such as drug discovery.” (Page 10, Section “Conclusion”, lines 2-5). This suggests that the model of a conformation generated by GeoMol can be used to determine whether or not it modulates a biological target, which speeds up drug discovery.
With respect to claim 15:
With respect to the recited inputting condition data into the machine learning platform, wherein the condition data is at least one conformation of the molecule, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc. For each molecular graph G, we have a variable-size set of low-energy ground truth 3D conformers
{
C
l
*
}
l
that we predict with a model
{
C
k
}
k
≝
ζ
(
G
)
.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests inputting condition data into the machine learning platform, where the condition data includes at least one conformation of the molecule.
With respect to claim 16:
With respect to the recited encoding discrete features of nodes and edge features with embedding layers, each edge feature including a first graph subset that has a chemical bond type and bond stereochemistry, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). Also, further discloses “It explicitly and deterministically distinguishes reflected structures (enantiomers) by solving tetrahedral stereocenters using oriented volumes and local chiral descriptors” (Page 3, Section “Our key contributions & model in a nutshell”, sixth bullet, lines 1-2). This suggests that GeoMol encodes node and edge features with embedding layers, where edge features include bond type and bond stereochemistry.
With respect to the recited applying a sequence of graph convolution blocks to the discrete features to obtain an embedding of the molecular graph of the molecule, Ganea et al. discloses “Given an input graph G, an MPNN [Gilmer et al., 2017, Battaglia et al., 2018, Yang et al., 2019] computes node embeddings
h
v
∈
R
d
,
∀
v
∈
V
using
T
layers of iterative message passing” (Page 4, Section “2.2 Message passing neural networks (MPNNs)”, paragraph 1, lines 1-2). Also, further discloses “we also compute a molecular embedding:
h
m
o
l
≝
M
L
P
(
∑
v
∈
V
h
v
)
.” (Pages 4-5, Section “2.2 Message passing neural networks (MPNNs)”, paragraph 1, lines 5-6). This suggests that message passing neural networks (MPNNs) are the graph convolution blocks used to obtain embeddings of the molecular graph of the molecule.
With respect to claim 19:
With respect to the recited further comprising a conformation discriminator, Ganea et al. discloses “In this work, we assume that the low-energy states are implicitly defined by the given dataset, i.e., our training data consist of molecular graphs and corresponding sets of energetically favorable 3D conformations. Low-energy structures are the most stable configurations and, thus, expected to be observed most often experimentally.” (Page 1, Section “Problem & importance”, paragraph 1, lines 7-12). This suggests that a conformation discriminator distinguishes between conformations based on low-energy states.
With respect to the recited controlling quality of generated objects, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). This suggests controlling the quality of generated conformers based on computed energies.
With respect to the recited assessing a likelihood of one or more conformations, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). Gauging the plausibility of generated conformers suggests assessing a likelihood of conformations.
With respect to the recited determining a quality of the one or more conformations based on potential energy estimations, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). This suggests computing energy estimations in order to determine a quality of conformations.
Claim 22 recites one or more non-transitory computer readable media storing instructions. Claim 23 recites a computer system comprising one or more processors and one or more non-transitory computer readable media storing instructions.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Therefore, the differences in the prior art were encompassed in known variations or in principle known in the prior art. The rationale would have been the predictable use of prior art elements according to their established functions. KSR 550 U.S. at 417.
For these reasons, the instant claims do not recite any new element or new function or unpredictable result, and the examiner invites the applicant to provide evidence demonstrating the novel or unobvious difference between the claimed limitations and those used in the prior art, as mere argument cannot take the place of evidence lacking in the record. Estee Lauder Inc. v. L’Oreal, S.A., 129 F .3d 588, 595 (Fed. Cir. 1997).
Claims 5-6, 9, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Winter et al. (CoRR, 2021, 1-6), as provided in the IDS filed 11/30/2022.
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 5:
With respect to the recited inputting molecule graph data of the molecule and a set of latent vectors into a generator, Ganea et al. discloses “We generate a representative set of low-energy 3D conformers from the input molecular graph.” (Figure 1). Also, further discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests that the generator takes in molecular graph data and latent vectors encoded using node and edge features to generate low-energy 3D conformers.
With respect to the recited distinguishing real conformations from generated conformations with predicted energy differences, Ganea et al. discloses “To gauge the plausibility of generated conformers, we compute the energies as defined by the MMFF force field within RDKit for conformers generated with ML-based methods before force field fine tuning.” (Supplementary Information, Page 19, Appendix K Energy calculations, lines 1-3). Also, further discloses “The energy values from GeoMol are the lowest among the ML methods, indicating greater stability of generated conformers, especially for the druglike molecules.” (Supplementary Information, Page 20, Appendix K Energy calculations, lines 1-3). This suggests that real conformations are distinguished from generated conformations based on their low energy values.
Ganea et al. does not disclose outputting a conformation of the molecule as a sequence of internal coordinates.
However, Winter et al. discloses “The conformation-independent part comprises a Graph Neural Network utilizing the molecular graph to extract node-level features for a given molecule. The conformation-dependent part utilizes these extracted node-level features either to encode the internal coordinates of a specific molecular conformation into a latent representation (conformation embedding) or to reconstruct a conformation by predicting the internal coordinates of sets of connected atoms, given their respective node features and a conformation embedding.” (Page 3, Section “2.2 Conformation Autoencoder”, lines 2-7). This suggests that node-level features from molecular graph data are used to encode internal coordinates of a molecular conformation.
Ganea et al. does not disclose mapping conformations into latent space.
However, Winter et al. discloses “The overall goal of the proposed model is to find functions
f
Θ
and
g
Θ
that map a conformation
Ξ
G
of a molecule
G
to and from a fixed-sized latent representation
z
Ξ
∈
R
F
z
, respectively.” (Page 2, Section “2.2 Conformation Autoencoder”, lines 1-2). This suggests functions that can map a conformation to a fixed-sized latent representation or latent space.
Ganea et al. does not disclose conforming the latent space to be similar to a prior distribution.
However, Winter et al. discloses “Our proposed model can easily be extended to a probabilistic generative model by employing the ideas from Kingma and Welling (2013), effectively defining the model as a variational auto encoder.” (Page 3, Section “2.2 Conformation Autoencoder”, lines 9-11). This suggests that the autoencoder model for molecular conformations can be extended to a probabilistic generative model that can conform latent space to be similar to a prior distribution.
With respect to claim 6:
With respect to the recited further comprising a conformation generation, Ganea et al. discloses “We propose GeoMol – an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers.” (Abstract, lines 6-8). This describes a machine learning model that generates molecular conformations, which suggests a conformation generation.
With respect to the recited predicting bond lengths and a bond-wise loss function weight of the first conformation, Ganea et al. discloses “If the corresponding ground truth conformer
C
*
is known, we feed those quantities into a negative log-likelihood loss, denote by
L
(
C
,
C
*
)
and detailed in appendix D. Similar to Senior et al. [2020], we fit distances using normal distributions and angles using von Mises distributions. This is a much faster approach compared to habitual RMSD losses that compare full conformers.” (Page 7, Section “2.5 An optimal transport (OT) loss function for diverse conformer generation”, paragraph 1, lines 4-9). Also, further discloses “Before (left) and after (right) introducing a matching loss to distinguish symmetric graph nodes. Hydrogen predictions in both groups are visibly improved.” (Figure 6). This suggests that bond lengths are predicted using normal distributions and bond-wise loss function weights are predicted in a loss function. Weights are adjusted to improve molecular predictions by tuning hyperparameters on the validation set (Supplementary Information, Page 15, Appendix D Details of the loss function, paragraph 2, lines 4-6).
With respect to the recited modulating bond length of the conformation to the predicted bond lengths, Ganea et al. discloses “Similar to Senior et al. [2020], we fit distances using normal distributions and angles using von Mises distributions. This is a much faster approach compared to habitual RMSD losses that compare full conformers.” (Page 7, Section “2.5 An optimal transport (OT) loss function for diverse conformer generation”, paragraph 1, lines 6-9). This suggests that bond length was adjusted based on prediction using normal distributions.
Ganea et al. does not disclose generating internal coordinates of a first conformation from the molecule graph data and noise.
However, Winter et al. discloses “We utilize the internal coordinate representation, also known as Z-matrix. In this notation, a molecules spatial arrangement (conformation)
Ξ
is defined by the set of distances
D
=
{
d
1
,
…
,
d
N
D
}
between bonded atoms (bond length), the angles
Ф
=
{
Φ
1
,
…
,
Φ
N
Ф
}
of three connected atoms (bond angles) and the torsion angles (dihedral angles)
Ѱ
=
{
ѱ
1
,
…
,
ѱ
N
D
}
of three consecutive bonds (see Figure 1). This representation is invariant to rotations and rigid translations and can always be transformed to and from Cartesian coordinates.” (Page 2, Section “2.1 Representing Molecular Conformations”, lines 3-9). This suggests that internal coordinates are generated from molecule data, including noise.
Ganea et al. does not disclose converting the internal coordinates to Cartesian coordinates for the first conformation.
However, Winter et al. discloses “We utilize the internal coordinate representation, also known as Z-matrix. In this notation, a molecules spatial arrangement (conformation)
Ξ
is defined by the set of distances
D
=
{
d
1
,
…
,
d
N
D
}
between bonded atoms (bond length), the angles
Ф
=
{
Φ
1
,
…
,
Φ
N
Ф
}
of three connected atoms (bond angles) and the torsion angles (dihedral angles)
Ѱ
=
{
ѱ
1
,
…
,
ѱ
N
D
}
of three consecutive bonds (see Figure 1). This representation is invariant to rotations and rigid translations and can always be transformed to and from Cartesian coordinates.” (Page 2, Section “2.1 Representing Molecular Conformations”, lines 3-9). This suggests that the internal coordinate representation can be converted to Cartesian coordinates.
Ganea et al. does not disclose computing the Cartesian coordinates for unit direction and unit normal vectors for the conformation.
However, Winter et al. discloses “We utilize the internal coordinate representation, also known as Z-matrix. In this notation, a molecules spatial arrangement (conformation)
Ξ
is defined by the set of distances
D
=
{
d
1
,
…
,
d
N
D
}
between bonded atoms (bond length), the angles
Ф
=
{
Φ
1
,
…
,
Φ
N
Ф
}
of three connected atoms (bond angles) and the torsion angles (dihedral angles)
Ѱ
=
{
ѱ
1
,
…
,
ѱ
N
D
}
of three consecutive bonds (see Figure 1). This representation is invariant to rotations and rigid translations and can always be transformed to and from Cartesian coordinates.” (Page 2, Section “2.1 Representing Molecular Conformations”, lines 3-9). This describes a Z-matrix to Cartesian conversion, which involves computation of Cartesian coordinates for unit direction and unit normal vectors.
With respect to claim 9:
With respect to the recited perform at least one optimization to correct local distance geometry of at least one molecular substructure, Ganea et al. discloses “Similar to Senior et al. [2020], we fit distances using normal distributions and angles using von Mises distributions. This is a much faster approach compared to habitual RMSD losses that compare full conformers.” (Page 7, Section “2.5 An optimal transport (OT) loss function for diverse conformer generation”, paragraph 1, lines 6-9). This suggests correcting distance geometry by fitting distances using normal distributions.
With respect to the recited operating a conformation discriminator to distinguish between real conformations of a molecule from synthetic conformations of the molecule, Ganea et al. discloses “In this work, we assume that the low-energy states are implicitly defined by the given dataset, i.e., our training data consist of molecular graphs and corresponding sets of energetically favorable 3D conformations. Low-energy structures are the most stable configurations and, thus, expected to be observed most often experimentally.” (Page 1, Section “Problem & importance”, paragraph 1, lines 7-12). This suggests that a conformation discriminator distinguishes between real conformations from synthetic conformations based on low-energy states.
Ganea et al. does not disclose operating a molecular graph generator to obtain molecular graph data and latent code data to construct a conformation of a molecule with a set of internal coordinates, to convert the internal coordinates into Cartesian coordinates.
However, Winter et al. discloses “The conformation-independent part comprises a Graph Neural Network utilizing the molecular graph to extract node-level features for a given molecule. The conformation-dependent part utilizes these extracted node-level features either to encode the internal coordinates of a specific molecular conformation into a latent representation (conformation embedding) or to reconstruct a conformation by predicting the internal coordinates of sets of connected atoms, given their respective node features and a conformation embedding.” (Page 3, Section “2.2 Conformation Autoencoder”, lines 2-7). Also, further discloses “This representation is invariant to rotations and rigid translations and can always be transformed to and from Cartesian coordinates.” (Page 2, Section “2.1 Representing Molecular Conformations”, lines 8-9). This describes operating a molecular graph generator, or Graph Neural Network, that takes in molecular graph data and latent code data encoded using node-level features to generate a molecular conformation with internal coordinates. This also suggests that internal coordinates can be converted into Cartesian coordinates.
Ganea et al. does not disclose operating a stochastic encoder to construct an irredundant latent space of latent data of input molecules and prevent mode collapse.
However, Winter et al. discloses “The overall goal of the proposed model is to find functions
f
Θ
and
g
Θ
that map a conformation
Ξ
G
of a molecule
G
to and from a fixed-sized latent representation
z
Ξ
∈
R
F
z
, respectively.” (Page 2, Section “2.2 Conformation Autoencoder”, lines 1-2). Also, further discloses “by training a probabilistic model on a large dataset of molecular conformations, we demonstrate how our model can be used to generate diverse sets of energetically favorable conformations for a given molecule.” (Abstract, lines 5-8). This describes operating an encoder to construct an irredundant latent space of latent representation data. This also suggests preventing mode collapse because the model generates diverse sets of conformations.
Ganea et al. does not disclose operating a latent variables discriminator to map conformations into the latent space and to make the latent space similar to a normal prior distribution.
However, Winter et al. discloses “Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart.” (Abstract, lines 1-5). Also, further discloses “Our proposed model can easily be extended to a probabilistic generative model by employing the ideas from Kingma and Welling (2013), effectively defining the model as a variational auto encoder.” (Page 3, Section “2.2 Conformation Autoencoder”, lines 9-11). This suggests operating a latent variables discriminator that maps conformations into latent space. The model can be extended to a probabilistic generative model that can conform latent space to be similar to a normal prior distribution.
It would have been prima facie obvious to one of ordinary skill in the art to combine the conformation discriminator disclosed by Ganea et al. with the molecular graph generator, stochastic encoder, and latent variables discriminator disclosed by Winter et al. One would be motivated to make this combination because when quantitatively analyzing how energetically reasonable the reconstructed conformations are to the input conformations, the median energetic difference turned out to be approximately 80 kcal/mol, which corresponds to small deviations from local minimas, without e.g. clashed of atoms (Pages 4-5, Section “3 Results and Discussion”, paragraph 1, lines 5-9). This means the model comprising of the molecular graph generator, stochastic encoder, and latent variables discriminator is capable of generating molecular conformations that are energetically similar to the input conformation with very little difference. There is a likelihood of success, since both teachings are components from methods of generating molecular conformations, which are well known in the field of computational chemistry.
With respect to claim 14:
Ganea et al. does not disclose operating a graph convolution block.
However, Winter et al. discloses “We utilize a Graph Neural Network to extract a node-level representation of a molecular graph.” (Page 3, Section “2.2.1 Molecular Graph Encoder”, paragraph 2, lines 1-2). This suggests that the Graph Neural Network is the graph convolution block.
Ganea et al. does not disclose update representations of nodes and edges of a molecule graph data.
However, Winter et al. discloses “Given a molecular graph with initial node and edge features defined by the atoms and bonds of the molecule, a GNN iteratively updates node embeddings by aggregating localized information of connected nodes respectively.” (Page 3, Section “2.2.1 Molecular Graph Encoder”, paragraph 2, lines 2-4). Also, further discloses “To incorporate edge attributes
e
i
,
j
(bond-type information) in the model we also utilize the so-called edge-conditioned graph convolution (EConv) layer (Simonovsky and Komodakis, 2017), defined by the following update rule:
h
i
'
=
Θ
h
i
+
∑
j
∈
N
(
i
)
h
j
∙
f
Θ
(
e
i
,
j
)
” (Page 3, Section “2.2.1 Molecular Graph Encoder”, paragraph 4, lines 1-3). This suggests that node and edge representations are updated.
Ganea et al. does not disclose update node states.
However, Winter et al. discloses “we use the Graph Attention Network (GAT) (Veličković et al., 2017) framework which updates the node embeddings
h
i
” (Page 3, Section “2.2.1 Molecular Graph Encoder”, paragraph 3, lines 1-3). This suggests that node states are updated.
Ganea et al. does not disclose update hidden states of edges.
However, Winter et al. discloses “To incorporate edge attributes
e
i
,
j
(bond-type information) in the model we also utilize the so-called edge-conditioned graph convolution (EConv) layer (Simonovsky and Komodakis, 2017), defined by the following update rule:
h
i
'
=
Θ
h
i
+
∑
j
∈
N
(
i
)
h
j
∙
f
Θ
(
e
i
,
j
)
” (Page 3, Section “2.2.1 Molecular Graph Encoder”, paragraph 4, lines 1-3). This suggests that hidden states of edges are also updated.
With respect to claim 18:
Ganea et al. does not disclose further comprising a latent variables discriminator.
However, Winter et al. discloses “Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart.” (Abstract, lines 1-5). This describes a latent variables discriminator that converts molecular graph data into latent variable representations before distinguishing conformations by similarity.
Ganea et al. does not disclose distinguishing generated latent codes of real conformations from noise.
However, Winter et al. discloses “Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart.” (Abstract, lines 1-5). This suggests distinguishing latent representations of real conformations from noise, where real conformations cluster together while noise split apart.
Ganea et al. does not disclose determining node-wise latent codes being independent of each other.
However, Winter et al. discloses “Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart.” (Abstract, lines 1-5). This suggests determining node-wise latent representations being independent of each other based on how similar their conformations are.
Ganea et al. does not disclose determining node-wise latent codes following the normal distribution.
However, Winter et al. discloses “Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart.” (Abstract, lines 1-5). Also, further discloses “Our proposed model can easily be extended to a probabilistic generative model by employing the ideas from Kingma and Welling (2013), effectively defining the model as a variational auto encoder.” (Page 3, Section “2.2 Conformation Autoencoder”, lines 9-11). This suggests operating a latent variables discriminator that maps conformations into latent representations. The model can be extended to a probabilistic generative model that can conform the latent representations to be similar to a normal distribution, therefore determining latent codes following the normal distribution.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Xu et al. (International Conference on Learning Representations, 2021, 1-17), as provided in the IDS filed 11/30/2022; refer to as Xu [A].
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 7:
With respect to the recited representing the molecular graph by nodes and edge feature sets, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests the molecular graph is represented by nodes and edge feature sets.
With respect to the recited setting each node to include a description of: atom type, charge, and chiral tag, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests that each node is set to include a description of atom type, charge, and chiral tag.
With respect to the recited setting each edge feature to include a first graph subset that has chemical bond type and bond stereochemistry, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests that each edge feature includes a graph subset representing chemical bond type and bond stereochemistry.
With respect to the recited setting each edge feature to include a second graph subset that has a spanning tree traversal process and having defining edge features to be in the spanning tree and information regarding whether a source node appears earlier in the spanning tree traversal process than a destination node, Ganea et al. discloses “We first fix one BFS traversal of the graph and assemble the LS in the order given by this traversal. If the current node is not part of a cycle, then we can perform the assembling as described before. However, when we first encounter a node that is part of a cycle of nodes
X
1
,
X
2
,
…
,
X
n
, we will jointly compute all the 3D coordinates of this cycle and attach the entire ring structure to the current partial conformer.” (Supplementary Information, Page 16, Appendix E Details of the full conformer assembly procedure at test time, paragraph 4, lines 3-7). Also, further discloses “The key step is assembling two sets of 3D points: the set containing the LS of node X (and possibly other atom coordinates added in previous steps), denoted as
S
X
∶
=
{
p
1
,
…
,
p
n
}
⊂
R
3
, and the set containing the LS of node Y, denoted as
S
Y
∶
=
{
q
1
,
…
,
q
m
}
⊂
R
3
. Assume that X and Y are connected by a bond/edge.” (Supplementary Information, Page 16, Appendix E Details of the full conformer assembly procedure at test time, paragraph 2, lines 1-4). Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). This suggests that the Breadth First Search (BFS) spanning tree traversal process for reconstructing a molecular conformation comprises of defining edge features while connecting the nodes. This process also considers whether the first encounter of a node appears before a cycle of nodes, including the destination node.
Ganea et al. does not disclose extending the molecular graph with auxiliary nodes and edges to make a proposed generative model.
However, Xu [A] discloses “We also follow the previous work (Simm & Hernández-Lobato, 2020) to expand the molecular graph with auxiliary bonds, which is elaborated in Appendix B. For the molecular 3D representation, each atom in
V
is assigned with a 3D position vector
r
∈
R
3
. We denote
d
u
v
=
r
u
-
r
v
2
as the Euclidean distance between the
u
t
h
and
v
t
h
atom. Therefore, we can represent all the positions
{
r
v
}
v
∈
V
as a matrix
R
∈
R
|
V
|
×
3
and all the distances between connected nodes
{
d
u
v
}
e
u
v
∈
ε
as a vector
d
∈
R
|
ε
|
.” (Page 3, Section “2.1 Problem Definition”, paragraph 1, lines 5-10). Also, further discloses “Since the bonds existing in the molecular graph are not sufficient to characterize a conformation, we pre-process the graphs by extending auxiliary edges” (Page 13, Appendix B Data Preprocess, lines 3-4). This suggests the molecular graph is extended with auxiliary edges as well as auxiliary nodes.
Ganea et al. does not disclose introducing virtual edges between second, third, and/or fourth neighboring nodes.
However, Xu [A] discloses “Specifically, the atoms that are 2 or 3 hops away are connected with virtual bonds, labeled differently from the real bonds of the original graph.” (Page 13, Appendix B Data Preprocess, lines 4-6). This suggests introducing virtual edges between nodes that are 2 or 3 hops away, which are the second or third neighboring nodes.
It would have been prima facie obvious to one of ordinary skill in the art to modify the teachings from Ganea et al. to incorporate the teachings of Xu [A]. One would be motivated to make this modification because experimental results show that the generative model disclosed by Xu [A] outperforms all previous state-of-the-art baselines on the standard benchmarks (Page 9, Section “5 Conclusion and Future Work”, lines 4-5). As a CNF-based model, CGCF holds much the higher generative capacity for both diversity and quality compared than VAE approaches (Page 8, Section “Results”, lines 2-4). There is a likelihood of success, since both methods generate molecular conformations and are well known in the field of computational chemistry.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Landrum G., rdkit.Chem.Descriptors3D module, RDKit: Open-Source Cheminformatics and Machine Learning, https://www.rdkit.org/docs/source/rdkit.Chem.Descriptors3D.html#, 2018, accessed on 6 January 2026, 1-3).
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 8:
Ganea et al. does not disclose estimating one or more of the following conformation properties for each generated molecule: asphericity, eccentricity, inertial shape factor, two normalized principal moments ratios, three principal moments of inertia, gyration radius or spherocity index.
However, Landrum discloses a software module that retrieves descriptors from a molecule’s 3D structure, including functions rdkit.Chem.Descriptors3D.Asphericity(*x, **y) for molecular asphericity (Page 1, lines 2-3), rdkit.Chem.Descriptors3D.Eccentricity(*x, **y) for molecular eccentricity (Page 1, lines 14-15), rdkit.Chem.Descriptors3D.InertialShapeFactor(*x, **y) for inertial shape factor (Page 1, lines 25-26), rdkit.Chem.Descriptors3D.NPR1(*x, **y) for normalized principal moments ratio 1 (Pages 1-2, lines 36-37), rdkit.Chem.Descriptors3D.NPR2(*x, **y) for normalized principal moments ratio 2 (Page 2, lines 9-10), rdkit.Chem.Descriptors3D.PMI1(*x, **y) for first (smallest) principal moment of inertia (Page 2, lines 18-19), rdkit.Chem.Descriptors3D.PMI2(*x, **y) for second principal moment of inertia (Page 2, lines 25-26), rdkit.Chem.Descriptors3D.PMI3(*x, **y) for third (largest) principal moment of inertia (Page 2, lines 32-33), rdkit.Chem.Descriptors3D.RadiusOfGyration(*x, **y) for radius of gyration (Page 2, lines 39-40), and rdkit.Chem.Descriptors3D.SpherocityIndex(*x, **y) for molecular spherocity index (Page 3, lines 11-12).
It would have been prima facie obvious to combine the method of Ganea et al. with the software module of Landrum. One would be motivated to make this combination because the module is part of RDKit, which is a collection of cheminformatics and machine learning software written in C++ and Python. This RDKit module comprises of functions that compute for conformation properties of a molecule, which is a known technique that is applicable to a known method of molecular conformation generation disclosed by Ganea et al. There is a likelihood of success, since both the method and the module are commonly used together to generate molecular conformations and are well known in the field of computational chemistry before the effective filing date of the claimed invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Xu et al. (Proceedings of the 38th International Conference on Machine Learning, 2021, 139, 1-13), as provided in the IDS filed 11/30/2022; refer to as Xu [B].
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 10:
Ganea et al. does not disclose determining a reconstruction loss between an original conformation of a molecule compared to a reconstructed conformation of the molecule by adversarial analysis between the molecular graph generator against the confirmation discriminator and latent variables discriminator.
However, Xu [B] discloses “we model the distribution of conformations
R
conditioning on molecular graph
G
(i.e.
p
(
R
|
G
)
) with a conditional variational autoencoder (CVAE) (Kingma & Welling, 2013), in which a latent variable
z
is introduced to model the uncertainty in molecule conformation generation. The CVAE model includes a prior distribution of latent variable
p
ѱ
(
z
|
G
)
and a decoder
p
Ѳ
(
R
|
z
,
G
)
to capture the conditional distribution of
R
given
z
. During training, we also involve an additional inference model (encoder)
q
Ф
(
z
|
R
,
G
)
. The encoder and decoder are jointly trained to maximize the evidence lower bound (ELBO) of the data log-likelihood” (Page 3, Section “3.1. Overview”, paragraph 1, lines 2-12). Also, further discloses “The ELBO can be interpreted as the sum of the negative reconstruction error
L
r
e
c
o
n
(the first term) and a latent space prior regularizer
L
p
r
i
o
r
(the second term).” (Page 3, Section “3.1. Overview”, paragraph 2, lines 1-3). This suggests conducting an adversarial analysis between the molecular graph generator against the conformation discriminator and latent variables discriminator. The decoder implies a molecular graph generator as it captures the conditional distribution, generating a reconstructed conformation
R
given latent variables discriminator
z
and original conformation
G
. The encoder implies a conformation discriminator as an inference model used to distinguish between conformations. Training both the encoder and decoder maximizes the evidence lower bound (ELBO), which improves adversarial robustness and is part of adversarial analysis. ELBO also comprises the determination of reconstruction loss
L
r
e
c
o
n
.
It would have been prima facie obvious to one of ordinary skill in the art to combine the teachings from Ganea et al. with the teachings of Xu [B]. One would be motivated to make this combination because experimental results demonstrate the superior performance of the ConfVAE framework disclosed by Xu [B] over all state-of-the-art baselines on several standard benchmarks (Page 9, Section “6. Conclusion”, lines 6-9). By incorporating an end-to-end training objective via bilevel optimization, we consistently achieved a better result on all four metrics in coverage and matching scores that measure diversity and quality, respectively (Page 7, Section “Results”, col. 2, lines 6-8). There is a likelihood of success, since both methods generate molecular conformations and are well known in the field of computational chemistry.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Mansimov et al. (Nature, Scientific Reports, 2019, 9(1), 1-15), as provided in the IDS filed 11/30/2022.
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 17:
With respect to the recited further comprising an encoder, Ganea et al. discloses “Given an input graph G, an MPNN [Gilmer et al., 2017, Battaglia et al., 2018, Yang et al., 2019] computes node embeddings
h
v
∈
R
d
,
∀
v
∈
V
using
T
layers of iterative message passing” (Page 4, Section “2.2 Message passing neural networks (MPNNs)”, paragraph 1, lines 1-2). This suggests that a Message Passing Neural Network (MPNN) is an encoder that computes node embeddings.
With respect to the recited obtaining a description of a conformation from molecular graph data of a molecule, Ganea et al. discloses “Our input is any molecular graph
G
=
(
V
,
E
)
with node and edge features,
x
v
∈
R
f
,
∀
v
∈
V
and
e
u
,
v
∈
R
f
,
∀
(
u
,
v
)
∈
E
representing atom types, formal charges, bond types, etc.” (Page 4, Section “Problem setup & notations”, paragraph 1, lines 1-3). The node and edge features are the descriptions obtained from molecular graph data of a molecule.
With respect to the recited transforming the conformation with a sequence of graph convolution blocks to obtain node-wise latent codes, Ganea et al. discloses “Given an input graph G, an MPNN [Gilmer et al., 2017, Battaglia et al., 2018, Yang et al., 2019] computes node embeddings
h
v
∈
R
d
,
∀
v
∈
V
using
T
layers of iterative message passing” (Page 4, Section “2.2 Message passing neural networks (MPNNs)”, paragraph 1, lines 1-2). This suggests MPNNs as a sequence of graph convolution blocks that transforms a molecular conformation into node embeddings, or node-wise latent codes.
Ganea et al. does not disclose wherein the latent codes are stochastic and sampled with reparameterization from a normal distribution parameterized by outputs of the encoder.
However, Mansimov et al. discloses “With the choice of the Gaussian latent variables
z
i
, we can use the reparameterization trick to compute the gradient of the stochastic approximation to the lower bound in Eq. (7) with respect to all the parameters of the three distributions. This property allows us to train this model on a large dataset using stochastic gradient descent (SGD).” (Page 4, Section “Training the conditional variational graph autoencoder”, paragraph 1, lines 1-4). Also, further discloses “the MPNN outputs a Normal distribution for each latent variable
z
i
.” (Page 4, Section “Posterior parameterization”, paragraph 2, lines 1-2). This suggests that the latent variables are stochastic and modeled as normal distributions to be sampled with reparameterization.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of Ganea et al. to incorporate the teachings of Mansimov et al. One would be motivated to make this modification because compared to other methods, the proposed CVGAE model always succeeds at generating the specified number of conformations for any of the molecules in the test set (Page 6, Section “Results”, lines 2-3). This means that the model disclosed by Mansimov et al. is consistent in producing desired conformations. There is a likelihood of success, since both methods generate molecular conformations and are well known in the field of computational chemistry.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, and further in view of Schutt et al. (The Journal of Chemical Physics, 2018, 148(24), 1-11), as provided in the IDS filed 11/30/2022.
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 20:
With respect to the recited further comprising a conformation discriminator, Ganea et al. discloses “In this work, we assume that the low-energy states are implicitly defined by the given dataset, i.e., our training data consist of molecular graphs and corresponding sets of energetically favorable 3D conformations. Low-energy structures are the most stable configurations and, thus, expected to be observed most often experimentally.” (Page 1, Section “Problem & importance”, paragraph 1, lines 7-12). This suggests that a conformation discriminator distinguishes between conformations based on low-energy states.
Ganea et al. does not disclose passing molecular graph embeddings through a plurality of SchNet layers to obtain node representations.
However, Schutt et al. discloses “SchNet is a variant of the earlier proposed Deep Tensor Neural Network (DTNN) and therefore shares a number of their essential building blocks. Among these are atom embeddings interaction refinements and atom-wise energy contributions. At each layer, the atomistic system is represented atom-wise being refined using pairwise interactions with the surrounding atoms. In the DTNN framework, interactions are modeled by tensor layers, i.e., atom representations and interatomic distances are combined using a parameter tensor.” (Page 2, Section “II. Method”, lines 1-10). This suggests that molecular graph embeddings are refined through SchNet layers to obtain node or atom representations.
Ganea et al. does not disclose obtaining one aggregated value for the whole molecular conformation.
However, Schutt et al. discloses “Finally, a given property
P
of a molecule or material is predicted from the obtained atom-wise representations. We compute atom-wise contributions
P
^
i
from the fully connected prediction network (see blue layers in Fig. 1). Depending on whether the property is intensive or extensive, we calculate the final prediction
P
^
by summing or averaging over the atomic contributions, respectively.” (Page 3, Section “E. Property prediction”, paragraph 1, lines 1-7). This suggests that node representations obtained from the SchNet layers are used to compute one aggregated value for the whole molecular conformation.
It would have been prima facie obvious to one of ordinary skill in the art to combine the method of Ganea et al. with the teachings of Schutt et al. One would be motivated to make this combination because not only does SchNet yield fast and accurate predictions of molecular properties, but it also allows for examining the learned representation using local chemical potentials (Page 9, Section “Conclusion”, paragraph 2, lines 10-12). There is a likelihood of success, since both the method and SchNet are commonly used in molecular discovery and are well known in the field of computational chemistry.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ganea et al. (NeurIPS, 2021, 1-13) as applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above, in view of Shi et al. (International Conference on Machine Learning, PMLR, 2020, 1-10).
Ganea et al. is applied to claims 1-4, 11-13, 15-16, 19, and 22-23 above.
With respect to claim 21:
Ganea et al. does not disclose determining an ability to synthesize generated molecular conformation, wherein the generated molecular conformation has at least one three dimensional restriction.
However, Shi et al. discloses “At the
i
t
h
transformation step, we first calculate the probabilities of all possible actions and sort them, and then select the top
k
ranked valid actions for each candidate graph in
S
i
-
1
,
j
in
S
. Once this is done for
k
graphs in
S
, the top
k
graphs among all the generated
k
2
graphs are then selected as the candidates for the next
(
i
+
1
)
t
h
transformation step. During this beam search, a translation branch will stop if
i
reaches the predefined maximum transformation step or
a
i
1
indicates a termination. In this scenario, the current graph will be added into a set
G
, and the whole beam search terminates once all translation branches stop. When the beam search finishes, the top
k
graphs in
G
, ranked by their likelihoods, will be collected as the final predicted graphs.” (Page 6, Section “3.3.3. Generation”, paragraph 2, lines 3-16). This suggests determining the ability of the top
k
graphs to synthesize through the beam search process. They are ranked by their likelihoods, which implies at least one three dimensional restriction for the generated molecular conformations.
It would have been prima facie obvious to one of ordinary skill in the art to combine the method of Ganea et al. with the teachings of Shi et al. One would be motivated to make this combination because experimental results show that G2Gs significantly outperforms existing template-fee approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of the state-of-the-art template-based approaches, but does not require domain knowledge and is much more scalable (Abstract, lines 15-21). There is a likelihood of success, since both methods are commonly used in drug discovery and are well known in the field of computational chemistry.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686