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 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application claims foreign priority to IN202321005947 filed 30 January 2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. In this action, claims 1-20 are examined as though they had an effective filing date of 30 January 2023. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 31 October 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 31 October 2023 are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
The term “desired” in claims 6, 13, and 19 is a relative term which renders the claim indefinite. The term “desired” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What is desired is subjective because it will be dependent upon the user/situation (MPEP 2173.05(b)).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1-7 are directed to a method and Claims 8-20 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitation - pre-processing, via the one or more hardware processors, the received dataset of molecules to obtain a training dataset of molecules; jointly training a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and generating one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE. Based on the broadest reasonable interpretation, processing a dataset, training variational autoencoders, and generating smiles using the variational autoencoders encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules. This limitation specifies the information going into the training in the judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 4 recites the limitation - wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space. This limitation specifies the generating judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 5 recites the limitation - wherein a decoder of the s-VAE generates one or more conditional novel small molecules. This limitation specifies the generating judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 6 recites the limitation - wherein the generated one or more conditional novel small molecules induce a desired gene expression. This limitation specifies the generating judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 7 recites the limitation - wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties. Based on the broadest reasonable interpretation, passing information through a filter to satisfy a property encompasses could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 8 recites the limitation - pre-process the received dataset of molecules to obtain a training dataset of molecules; jointly train a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and generate one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE. Based on the broadest reasonable interpretation, processing a dataset, training variational autoencoders, and generating smiles using the variational autoencoders encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules. This limitation specifies the information going into the training in the judicial exception of claim 8. The refined limitation indicated still represents a judicial expectation.
Claim 11 recites the limitation - wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space. This limitation specifies the generating judicial exception of claim 8. The refined limitation indicated still represents a judicial expectation.
Claim 12 recites the limitation - wherein a decoder of the s-VAE generates one or more conditional novel small molecules. This limitation specifies the generating judicial exception of claim 8. The refined limitation indicated still represents a judicial expectation.
Claim 13 recites the limitation - wherein the generated one or more conditional novel small molecules induce a desired gene expression. This limitation specifies the generating judicial exception of claim 8. The refined limitation indicated still represents a judicial expectation.
Claim 14 recites the limitation - wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties. Based on the broadest reasonable interpretation, passing information through a filter to satisfy a property encompasses could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 15 recites the limitation - pre-processing the received dataset of molecules to obtain a training dataset of molecules; jointly training a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and generating one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE. Based on the broadest reasonable interpretation, processing a dataset, training variational autoencoders, and generating smiles using the variational autoencoders encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 17 recites the limitation - wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules. This limitation specifies the information going into the training in the judicial exception of claim 15. The refined limitation indicated still represents a judicial expectation.
Claim 18 recites the limitation - wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space, and wherein a decoder of the s-VAE generates one or more conditional novel small molecules. These limitations specifies the generating judicial exception of claim 15. The refined limitation indicated still represents a judicial expectation.
Claim 19 recites the limitation - wherein the generated one or more conditional novel small molecules induce a desired gene expression. This limitation specifies the generating judicial exception of claim 15. The refined limitation indicated still represents a judicial expectation.
Claim 20 recites the limitation - wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties. Based on the broadest reasonable interpretation, passing information through a filter to satisfy a property encompasses could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of processing, generating, and comparing data and utilizing generic machine learning algorithms that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites an input/output interface; hardware processors; receiving a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database.
Claim 2 recites wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
Claim 8 recites an input/output interface; receive a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database; memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory.
Claim 9 recites wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
Claim 15 recites One or more non-transitory machine-readable information storage mediums comprising one or more instructions executed by one or more hardware; and receiving, via an input/output interface, a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database.
Claim 16 recites wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
There are no limitations that indicate that the claimed processing, generating, and comparing data and utilizing generic machine learning algorithms require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed processing, generating, and comparing data and utilizing generic machine learning algorithms require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Lopez et al. (2020, Molecular Systems Biology, Vol 16: 1-21) teach the utilization data concerning gene expression and drug molecules related to disease through generic computers (Page 3, Figure 1: data forms such as biological sequences, molecules, gene expression, or imaging data; also see Figure 2 on Page 4; Page 17, Column 1, Paragraph 3: software libraries are available that already implement many of the operations needed by DGMs) is well understood routine and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Born et al. (2021, iScience, Vol. 24: 1-29), in view of Krishnan et al. (2022, Journal of Chemical information and Modeling, Vol. 62: 5100−5109). Italicized text from reference art.
Applicable Claims include:
Claim 1. A processor-implemented method comprising: (Claim 1.i) receiving, via an input/output interface, a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database; (Claim 1.ii) pre-processing, via the one or more hardware processors, the received dataset of molecules to obtain a training dataset of molecules; (Claim 1.iii) jointly training, via the one or more hardware processors, a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and (Claim 1.iv) generating, via the one or more hardware processors, one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE.
Claim 2. The processor-implemented method of claim 1, wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
Claim 3. The processor-implemented method of claim 1, wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules.
Claim 4. The processor-implemented method of claim 1, wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space.
Claim 5. The processor-implemented method of claim 1, wherein a decoder of the s-VAE generates one or more conditional novel small molecules.
Claim 6. The processor-implemented method of claim 1, wherein the generated one or more conditional novel small molecules induce a desired gene expression.
Claim 7. The processor-implemented method of claim 1, wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties.
Claim 8. A system comprising: (Claim 8.i) an input/output interface to receive a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database; a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to: (Claim 8.ii) pre-process the received dataset of molecules to obtain a training dataset of molecules; (Claim 8.iii) jointly train a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and (Claim 8.iv) generate one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE.
Claim 9. The system of claim 8, wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
Claim 10. The system of claim 8, wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules.
Claim 11. The system of claim 8, wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space.
Claim 12. The system of claim 8, wherein a decoder of the s-VAE generates one or more conditional novel small molecules.
Claim 13. The system of claim 8, wherein the generated one or more conditional novel small molecules induce a desired gene expression.
Claim 14. The system of claim 8, wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties.
Claim 15. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: (Claim 15.i) receiving, via an input/output interface, a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database; (Claim 15.ii) pre-processing the received dataset of molecules to obtain a training dataset of molecules; (Claim 15.iii) jointly training a simplified molecular input line entry system (SMILES) variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules; and (Claim 15.iv) generating one or more conditional novel small molecules in SMILES format from the received gene expression profile using trained s-VAE and p-VAE.
Claim 16. The one or more non-transitory machine-readable information storage mediums of claim 15, wherein the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment.
Claim 17. The one or more non-transitory machine-readable information storage mediums of claim 15, wherein the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules.
Claim 18. The one or more non-transitory machine-readable information storage mediums of claim 15, (Claim 18.i) wherein an encoder of the p-VAE learns to project the received gene expression profiles into a latent space, and (Claim 18.ii) wherein a decoder of the s-VAE generates one or more conditional novel small molecules.
Claim 19. The one or more non-transitory machine-readable information storage mediums of claim 15, wherein the generated one or more conditional novel small molecules induce a desired gene expression.
Claim 20. The one or more non-transitory machine-readable information storage mediums of claim 15, wherein the one or more conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties.
Regarding Claims 1, 8, and 15, Born et al. teach (Claim 1.i) receiving a gene expression profile in a cell-specific manner as an input and a dataset of molecules from a predefined drug-like small molecule database (Page 2, Paragraph 2: we guide the learning process solely by transcriptomic profiles of cancer cells; Page 3, Paragraph 2: The SMILES VAE was pretrained for 10 epochs on ~1.4 million structures from ChEMBL). Utilizing the data is interpreted as equivalent to receiving the data. Born et al. teach suggest (Claim 1.ii) pre-processing the received dataset of molecules to obtain a training dataset of molecules. Data on molecules are received and could come in any form (structures, graphs, names) and must be converted to strings (SMILES) for input into the model. Therefore, it would be obvious to have a preprocessing step for the molecule data set (i.e. conversion to SMILES). Born et al. teach (Claim 1.iii) jointly training a SMILES variational autoencoder (s-VAE) and a profile variational autoencoder (p-VAE) with the obtained training dataset of molecules (Page 2, Paragraph 3: the encoder of the profile VAE is combined with the decoder of the molecule VAE and exposed to a joint retraining that is optimized using a policy gradient regime with a reward coming from the critic module; Page 15, Figure S1: A biomolecular profile VAE (PVAE) was pretrained on RNA-Seq data from TCGA to encode a transcriptomic profile into a latent code. Similarly, a sequential compound generator VAE (SVAE) was trained to encode and decode SMILES representations of molecules. PVAE and SVAE are combined to obtain a conditional molecule generator. The combination is achieved to fuse the latent spaces of omics profiles and molecules to a joint, multimodal representation). Born et al. teach (Claim 1.iv) generating a conditional novel small molecule in SMILES format from the received gene expression profile using trained s-VAE and p-VAE (Page 2, Paragraph 3: The goal of the optimization is to tune the generative model such that it generates (novel) compounds that have maximal efficacy against a given biomolecular profile; Page 15, Figure S1: Molecules are generated directly as SMILES sequences). Additionally, Born et al. teach the method is performed by a computer which inherently contains program code, memory, including non-transitory computer readable mediums, and at least on processor to carry out the functions of the method (Page 18, Paragraph 4: All models were implemented in PyTorch 1.0 and trained on a cluster equipped with POWER8 processors and a NVIDIA Tesla P100). Additionally, the computer implementation confirms the presence of an input/output interface (Page 18, Paragraph 6: the model's output is conditioned on the previous ground truth sample as opposed to its generated output; Page 18, Paragraph 1: our implementation uses an efficient representation of varying lengths' inputs in PyTorch). Claim 8 recite the limitations of claim 1 directed to a system and claim 15 recites the limitations of claim 1 directed to a computer readable medium. Additionally, it would be obvious to in have an input/output interface given data in input and output from the modeling as part of the invention.
Regarding Claims 2, 9, and 16, Born et al. teach the gene expression profile includes molecular signature of a disease and an associated relationship with a phenotypic environment (Page 2, Paragraph 2: we herein propose a novel framework to generate lead compound candidates solely based on a tumor’s metabolic signature. we guide the learning process solely by transcriptomic profiles of cancer cells). This indicates the transcriptomic input (i.e. a phenotypic environment) is related to a molecular signature of a disease (tumor’s metabolic signature). Claim 9 recite the limitations of claim 2 directed to a system and claim 16 recites the limitations of claim 2 directed to a computer readable medium.
Regarding Claims 3, 10, and 17, Born et al. teach the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules (Page 17, Paragraph 1: we define the set of states as all possible SMILES strings). Claim 10 recite the limitations of claim 3 directed to a system and claim 17 recites the limitations of claim 3 directed to a computer readable medium.
Regarding Claims 4 and 11, Born et al. teach an encoder of the p-VAE learns to project the received gene expression profiles into a latent space (Page 21, Paragraph 1: these results suggest that the PVAE learns to embed gene expression profiles (GEPs) meaningfully into a latent space). Claim 11 recite the limitations of claim 4 directed to a system.
Regarding Claim 5 and 12, Born et al. teach a decoder of the s-VAE generates one or more conditional novel small molecules (Page 2, Paragraph 3: Our framework is depicted in Figure 1B and consists of a conditional molecule generator. The goal of the optimization is to tune the generative model such that it generates (novel) compounds; Page 23, Figure S6: sample of 12 molecular structures produced with the SVAE). Claim 12 recite the limitations of claim 5 directed to a system.
Regarding Claims 6, 13, and 19, Born et al. teach the generated one or more conditional novel small molecules induce a desired gene expression (Page 4, Paragraph 3: the IC50 distribution of candidate compounds proposed by the generative model were successfully shifted toward higher efficacy. a significant portion of molecules generated from the optimized model were assigned an IC50 value below 1mM, whereas only 2%–5% of the candidates generated by the baseline model were classified as effective). Demonstrating the molecules were more effective is interpreted as indicating the molecules induce a desired gene expression. Claim 13 recite the limitations of claim 6 directed to a system and claim 19 recites the limitations of claim 6 directed to a computer readable medium.
Regarding Claims 7, 14, and 20, Born et al. teach the conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties (Page 6, Paragraph 2: In the above comparisons, the search space was restricted to compounds with known anticancer properties; Page 8, Paragraph 1: Moreover, the generated anticancer compounds were found to have a significantly higher Tanimoto similarity to anticancer drugs than to either ChEMBL molecules (p < 0.01, one-sided MWU) or molecules generated without the RL optimization, i.e. from the SVAE (p < 0.01, one-sided MWU). PaccMann can seemingly drive the molecule generation away from ordinary bioactive compounds such as in ChEMBL, more toward mimicking the properties of anticancer drugs; also see Page 8, Figure 4). Claim 14 recite the limitations of claim 7 directed to a system and claim 20 recites the limitations of claim 7 directed to a computer readable medium.
Regarding Claim 18, Born et al. teach (Claim 18.i) an encoder of the p-VAE learns to project the received gene expression profiles into a latent space. This limitation is interpreted as equivalent to the limitation of claim 4 directed to a computer readable medium (see Born et al. teachings of claim 4 above). Born et al. teach (Claim 18.ii) a decoder of the s-VAE generates one or more conditional novel small molecules. This limitation is interpreted as equivalent to the limitation of claim 5 directed to a computer readable medium (see Born et al. teachings of claim 5 above).
Born et al. do not explicitly teach pre-processing the dataset of molecules (Claim 1.ii).
Regarding Claims 1, 8, and 15, Krishnan et al. teach (Claim 1.i) receiving a dataset of molecules from a predefined drug-like small molecule database (Page 5102, Column 2, Paragraph 4: The data set of druglike small molecules in SMILES format was obtained from the ChEMBL database). Krishnan et al. teach (Claim 1.ii) pre-processing the received dataset of molecules to obtain a training dataset of molecules (Page 5102, Column 2, Paragraph 4: The SMILES data set was preprocessed by following the procedure from our previous study). Krishnan et al. teach (Claim 1.iii) jointly training a SMILES variational autoencoder and a second variational autoencoder with the obtained training dataset of molecules (Page 5101, Figure 1). The VAEs are trained are trained together, which is interpreted as joint training. Krishnan et al. teach (Claim 1.iv) generating a conditional novel small molecule in SMILES format from the received data using trained s-VAE and p-VAE (Page 5101, Figure 1: SMILES is output of model; Page 5105, Column 1, Paragraph 2: Generating Novel Small Molecules against JAK2 and DRD2). Krishnan et al. teach the methods are computer based (Page 5108, Column 1: The code used to generate results shown in this study is available).
Regarding Claims 3, 10, and 17, Krishnan et al. teach the simplified molecular input line entry system (SMILES) is a string-based representation of the molecules (Page 5104, Column 2, Paragraph 1: decoding SMILES strings).
Regarding Claim 5 and 12, Krishnan et al. teach a decoder of the s-VAE generates one or more conditional novel small molecules (Page 5102, Column 1, Paragraph 2: a conditional molecule generator (a combination of the pretrained graph and SMILES-VAEs)).
Regarding Claims 7, 14, and 20, Krishnan et al. teach the conditional novel small molecules is passed through one or more physico-chemical filters to satisfy one or more drug-like properties (Page 5105, Column 2, Paragraph 3: A small molecule was considered as a hit, if the feature overlap score of the molecule with the target pharmacophore was at least half of the maximum feature overlap score. The hits among the generated small molecules were filtered).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Krishnan et al. with Born et al. Krishnan et al. teach novel modeling methods that were successful at designing drugs for targets (Page 5102, Column 1, Paragraph 1: Using this method, molecules were designed against two well studied protein targets, JAK2 and DRD2. The method could produce both similar and identical molecules compared to the existing inhibitors, while also retaining diversity. The generated molecules also preserved features of the existing inhibitors), which is a major focus of Born et al. and the instant application. Born et al. teach novel modeling methods that were successful at designing drugs for targets (Page 9, Paragraph 1: In this work we presented PaccMannRL, a novel framework for molecular generation that enables us to condition on the transcriptomic profile of the target. We demonstrated that our proposed generative model is able to produce candidate compounds with high predicted efficacy against a given target profile, even if this profile was never seen during training), which is a major focus of Krishnan et al. and the instant application. Additionally, it would have been obvious to swap the expression profile of Born et al. for the protein active site information of Krishnan et al. because Born et al. demonstrated it worked well to design drugs and the modeling was very closely related (see above). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field - use biological data input into variational autoencoders to predict drug molecules.
Double Patenting
No double patenting issues are associated with the instant claims.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE H ELKINS whose telephone number is (571)272-2649. The examiner can normally be reached Monday-Friday 8-5PM.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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