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 .
Election/Restrictions
Applicant's election with traverse of Group I, Claims 1-7 and 9-15, in the reply filed on 4/2/2026 is acknowledged. The traversal of the restriction requirement between Group I, Claims 1-7 and 9-15, and Group II, Claim 8, is on the grounds that (1) Group II provides a specific comparison methodology configured to evaluate the drug ranking methods; (2) Group II requires “obtaining a ranking by the current drug ranking method”, which encompasses the technical docking and scoring aspects of Group I; and (3) Group I and II are directed to the same specialized field of bioinformatics by improving the reliability of drug-target binding predictions.
This is not found persuasive for the following reasons: Claim 8 recites “the comparison method is configured to compare a plurality of drug ranking methods…”, “executing the following steps for a current drug ranking method of the drug ranking methods”, “according to a current benchmark drug target protein corresponding to the current benchmark drug, ranking the drugs to obtain a ranking by the current drug ranking method”, and “obtaining a drug ranking of the current benchmark drug according to the ranking”. Under the broadest reasonable interpretation (BRI) of the claim, the “current drug ranking method” could be any reasonable method of ranking a drug, and is not limited to the docking and scoring method of Group I. Therefore, due to the difference in drug ranking methodologies, Group II requires a search that is significantly different from that of Group I. Additionally, while both groups are drawn to methods of improving the reliability of drug-target binding predictions, as currently recited, only the novel ranking algorithm (Group I) requires docking and scoring. Therefore, as currently recited, Group II does not evaluate the efficacy of Group I, but rather the efficacy of any drug ranking methodology.
For at least the reasons above, the restriction requirement is still deemed proper and is therefore made FINAL.
Group II, Claim 8, and Group III, claims 16-20, are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction requirement in the reply filed on 4/2/2026.
Status of the Claims
Claims 1-7 and 9-15 are pending and under consideration in this action. Claims 8 and 16-20 are withdrawn from consideration.
Priority
The instant application claims domestic benefit to U.S. Provisional Application No. 63/222,438, filed 7/16/2021, as reflected in the filing receipt mailed 3/10/2023. The claim for domestic benefit for claims 1-7 and 9-15 is acknowledged. As such, the effective filing date of claims 1-7 and 9-15 is 7/16/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/11/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
It is noted that certain references have not been considered and are lined-through, as they do not comply with the requirements set forth in 37 CFR 1.97. A copy of the eESR dated 2022-12-06 (NPL #1) was not provided with the submission of the IDS.
It is further noted that certain references lack appropriate article numbers and/or page numbers (NPL #2 and #3). The Examiner has annotated those references herein. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submissions to the office.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (see Para. [0132]). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The listing of references in the Specification is not a proper information disclosure statement (see at least Para. [0047], [0049], [0131]-[0132], and [0134]). 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.
Claim Objections
Claims 5-6 and 13-14 are objected to because of the following informalities:
Claims 5 and 13 recite the phrase “executing following steps for a current feature…” in step (a1), which should be corrected to “executing the following steps for a current feature…”, to include the missing “the” for clarity.
Claims 6 and 14 recite the phrase “executing following steps for a current feature…” in step (c1), which should be corrected to “executing the following steps for a current feature…”, to include the missing “the” for clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 9-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 9 recite the limitation “according to a plurality of benchmark drugs in the drugs, a plurality of benchmark drug target proteins corresponding to the benchmark drugs, and a plurality of true binding poses and a plurality of decoy poses generated by docking the drugs with the benchmark drug target proteins, obtaining a statistics distribution pair for each of at least one feature” in step (a) of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It is unclear if the recited “drugs” corresponds to the “plurality of drugs” recited in the preamble. The Specification (see Para. [0008]) discloses that a plurality of benchmark drugs is obtained from the plurality of drugs, but it is unclear if this is the intended interpretation. This rejection can be overcome by amendment of claims 1 and 9 to clarify the recitation of the “drugs”. Claims 2-7 and 10-15 are also rejected due to their dependency from claims 1 and 9.
Claims 1 and 9 recite the limitation “docking the protein target for each of the drugs to obtain a plurality of poses and at least one feature value for each of the poses, wherein the at least one feature value corresponds to the at least one feature” in step (b) of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. As recited, it appears that the protein target is docked into each of the drugs. However, according to the Specification (see at least Para. [0059]-[0060]), each of the drugs is docked to the protein target to generate 20 poses for each of the drugs. Examiner suggests modification of the limitation to recite “docking each of the drugs to the protein target to obtain a plurality of poses and at least one feature value for each of the poses, wherein the at least one feature value corresponds to the at least one feature” or similar, to clarity that the drugs are docked into the protein target and not the other way around. Claims 2-7 and 10-15 are also rejected due to their dependency from claims 1 and 9.
Claims 3 and 11 recite the limitation “wherein the at least one feature comprises a ratio of a heavy atom of an assigned target residue of a protein encountered by a screened drug…” in lines 2-3 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It is unclear what ratio is being calculated, as the claim only appears to recite one part of the ratio (i.e., only the heavy atom and not what the heavy atom is compared to). The specification (see Para. [0068]-[0069]) reiterates the claim language and further discloses that the distance between the pose and the active site residue is a distance between the center of mass of the heavy atom of the drug and the center of mass of the heavy atoms of all the active site residues. It appears that the ratio could be a comparison of distances; however, it is unclear if this is the intended interpretation, as there is no ratio reflected in the claim limitation. Clarification through clearer claim language is respectfully requested.
Claims 3 and 11 also recite the limitation “wherein the at least one feature comprises a ratio of a heavy atom of an assigned target residue of a protein encountered by a screened drug, wherein encountered by is defined as the heavy atom of the assigned target residue of the protein is in contact with the heavy atom of the screened drug within 4 Å” in lines 3-4 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It is unclear if the protein encountering the screened drug is intended to further limit the docking (step (b)) in claims 1 and 9. The Specification (see Para. [0068]) reiterates the claim language but does not clarify the encountering step. If the protein encountering the screened drug is limiting the docking step, Examiner suggests amendment of claims 3 and 11 to actively recite docking of the screened drug to the protein, instead of the protein encountered by a screened drug.
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 1-7 and 9-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-7 are directed to a method, and claims 9-15 are directed towards a system, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
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, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claims 1 and 9 recite a mathematical concept (i.e., calculating a distribution) in “according to a plurality of benchmark drugs in the drugs, a plurality of benchmark drug target proteins corresponding to the benchmark drugs, and a plurality of true binding poses and a plurality of decoy poses generated by docking the drugs with the benchmark drug target proteins, obtaining a statistics distribution pair for each of at least one feature, wherein the statistics distribution pair of each of the at least one feature comprises a true binding pose statistics distribution and a decoy pose statistics distribution”; a mental process (i.e., evaluating data to determine a score) in “according to the at least one feature value of each of the poses and the statistics distribution pair of each of the at least one feature, obtaining a score of each of the poses for each of the drugs so as to obtain a plurality of pose scores of each of the drugs”; and a mental process (i.e., evaluating scores for ranking) in “ranking the drugs according to the pose scores of each of the drugs”.
Claims 2 and 10 recite a mental process (i.e., an evaluation of the feature) in “wherein the at least one feature comprises a binding affinity, a distance between a pose and an assigned target residue, and a size of a pose cluster”.
Claims 3 and 11 recites a mental process (i.e., an evaluation of the feature) in “wherein the at least one feature comprises a ratio of a heavy atom of an assigned target residue of a protein encountered by a screen drug, wherein encountered is defined as the heavy atom of the assigned target residue of the protein is in contact with the heavy atom of the screened drug within 4 Å”.
Claims 4 and 12 recite a mental process (i.e., an evaluation of the feature) in “wherein the at least one feature comprises a distance between a pose and at least one active site residue”.
Claims 5 and 13 recite a mathematical concept (i.e., calculating a ratio) in “according to a plurality of first feature values of the true binding poses corresponding to the current feature, in a plurality of first value intervals, calculating a first ratio of the true binding poses in each of the first value intervals to obtain a preliminary true binding pose statistics distribution corresponding to the current feature”; a mathematical concept (i.e., calculating a ratio) in “according to a plurality of second feature values of the decoy poses corresponding to the current feature, in a plurality of second value intervals, calculating a second ratio of the decoy poses in each of the second value intervals to obtain a preliminary decoy pose statistics distribution corresponding to the current feature”; a mathematical concept (i.e., performing fitting) in “fitting the preliminary true binding pose statistics distribution corresponding to the current feature by a first fitting function to obtain the true binding pose statistics distribution corresponding to the current feature; fitting the preliminary decoy pose statistics distribution corresponding to the current feature by a second fitting function to obtain the decoy pose statistics distribution corresponding to the current feature”; and a mental process (i.e., an evaluation of the features to determine if the method is repeated) in “repeatedly executing the step (a1) until all the at least one feature is processed”.
Claims 6 and 14 recites mathematical concepts (i.e., determining a probability) in “applying a current feature value corresponding to the current feature in the at least one feature value into the true binding pose statistics distribution corresponding to the current feature so as to obtain a first probability” and “applying the current feature value corresponding to the current feature in the at least one feature value into the decoy pose statistics distribution corresponding to the current feature so as to obtain a second probability”; a mathematical calculation in “dividing the first probability by the second probability to obtain a value and deriving the logarithm of the value so as to obtain a current feature score corresponding to the current feature”; a mental process (i.e., an evaluation of the features to determine if the method is repeated) in “repeatedly executing the step (c1) until all the at least one feature is processed”; and a mathematical concept (i.e., summing scores) in “after all the at least one feature is processed, summing up all the current features scores obtained from the steps (c1)-(c2) so as to obtain the score of the current pose”.
Claims 7 and 15 recite a mental process (i.e., ranking drugs by score) in “wherein the step (d) comprises: raking the drugs in a high-to-low manner according to a highest score among the pose scores of each of the drugs”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
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, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. 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.
Specifically, claims 1 and 9 involve nothing more than obtaining a statistics distribution for true and decoy poses, obtaining a score for the poses, and ranking the drugs according to the pose scores. The step reciting obtaining a statistics distribution for true binding poses and decoy poses is, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0073]) discloses the generation of the statistics distribution by binning binding affinity values within an interval to generate a distribution (see also. Fig. 3A for a statistics distribution example). Additionally, since there are no specifics in the methodology, the steps reciting obtaining a score for the poses, and ranking the drugs according to the pose scores, are something that under BRI, one could perform mentally. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
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)). 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 following independent claims recite limitations that equate to additional elements:
Claims 1 and 9 recite “a processing unit” and “docking the protein target for each of the drugs to obtain a plurality of poses and at least one feature value for each of the poses, wherein the at least one feature value corresponds to the at least one feature”.
Regarding the above cited limitation in claims 1 and 9 of (i) a processing unit. This limitation requires only a generic computer component, which does not improve computer technology. Therefore, this limitation equates to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitation in claims 1 and 9 of (ii) docking the protein target for each of the drugs to obtain a plurality of poses and at least one feature value for each of the poses, wherein the at least one feature value corresponds to the at least one feature. This limitation equates to insignificant, extra-solution activity of mere data gathering because this limitation gathers data before the recited judicial exceptions of obtaining a score for each of the poses of each of the drugs and ranking the drugs according to the pose scores (see MPEP § 2106.04(d)). As such, claims 1-7 and 9-15 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
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. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitation in claims 1 and 9 of (i) a processing unit. This limitation equates to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitation in claims 1 and 9 of (ii) docking the protein target for each of the drugs to obtain a plurality of poses and at least one feature value for each of the poses, wherein the at least one feature value corresponds to the at least one feature. This limitation, when viewed individually and in combination, is a well-understood, routine and conventional (WURC) limitation as taught by Fourches et al. (Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark. J. Chem. Inf. Model. 55(1): 63-71 (2015)). Fourches et al. discloses a hybrid docking and scoring workflow to rank steroid ligands for an engineered digoxigenin-binding protein (Abstract). Fourches et al. further discloses that all compounds were docked, and ranked according to their respective docking scores. Table 1 summarizes the docking scores obtained for the three top-ranking poses of each ligand (Pg. 66, Col. 1, Para. 1 and Pg. 66, Table 1). Fourches et al. further discloses teaches that in order to identify additional analogues with potential affinity for the engineered protein, they searched the ZINC database comprising ∼2.4 million druglike compounds. Specifically, they searched for steroid compounds with a high pairwise structural similarity, and identified a set of 5386 compounds with a pairwise Tanimoto similarity coefficient ≥ 0.9 (limitation (ii)) (Pg. 69, Col. 2, Para. 4).
These 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 instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-7 and 9-15 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 7, 9, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fourches et al. (Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark. J. Chem. Inf. Model. 55(1): 63-71 (2015); published 12/18/2014).
Regarding claims 1 and 9, Fourches et al. teaches a hybrid docking and scoring workflow to rank steroid ligands of an engineered digoxigenin-binding protein (i.e., a drug ranking method executed by a processing unit, wherein the drug ranking method is configured to rank a plurality of drugs according to a protein target) (Abstract). Fourches et al. further teaches that the Schrodinger suite was used to generate an ensemble of nativelike and decoy poses for the DIG cognate ligand using the 4J8T (DIG10.2 protein) PDB structure. As many as 1,428 and 530 poses with root-mean-square deviations (RMSDs) smaller than 4 Å from the native pose were generated using Glide SP and Glide XP, respectively, on the basis of the default parameters. For the purpose of modeling, they defined an RMSD threshold of 2 Å to discriminate nativelike poses from decoys. This threshold was consistent with the gap in the distribution plot of poses generated by redocking of the cognate ligand. Fig. 2 highlights the frequency distribution of poses vs. RMSD, where native like poses are shown in green (<2 Å RMSD), and decoys are shown in red (2-4 Å RMSD) (i.e., according to a plurality of benchmark drugs in the drugs, a plurality of benchmark drug target proteins corresponding to the benchmark drugs, and a plurality of true binding poses and a plurality of decoy poses generated by docking the drugs with the benchmark drug target proteins, obtaining a statistics distribution pair for each of at least one feature, wherein the statistics distribution pair of each of the at least one feature comprises a true binding pose statistics distribution and a decoy pose statistics distribution) (Pg. 64, Col. 2, Para. 3 – Pg. 65, Col. 1, Para. 1 and Pg. 65, Fig. 2). Fourches et al. further teaches that all 10 compounds were docked using both Glide SP and Glide XP, and ranked according to their respective Glide docking scores. Table 1 summarizes the docking scores obtained for the three top-ranking poses of each ligand (Pg. 66, Col. 1, Para. 1 and Pg. 66, Table 1). Fourches et al. further teaches that in order to identify additional DIG analogues with some potential affinity for the engineered protein, they searched the ZINC database comprising ∼2.4 million druglike compounds. Specifically, they searched for steroid compounds with a high pairwise structural similarity to DIG. They identified a set of 5,386 compounds with a pairwise Tanimoto similarity coefficient ≥ 0.9. These compounds were docked using the Glide SP scoring function, and only those poses with random forest pose classifier scores higher than 0.7 were retained. The chemical structures of two of these compounds, ZINC04655335 and ZINC71770245, are shown in Table 4; their SP scores are −8.83 and −8.71, respectively (i.e., docking the protein target for each of the drugs to obtain a plurality of poses and at least one feature value for each of the poses, wherein the least at one feature value corresponds to the at least one feature) (Pg. 69, Col. 2, Para. 4). Fourches et al. further teaches that with SP, the best-scored pose (SP score = −10.42) was retrieved for the ligand cs335, whereas the worst pose (SP score = −7.62) was associated with ligand cs339. With the XP scoring function, the best pose (XP score = −13.34) was also retrieved for ligand cs335, whereas ligand cs333 obtained the worst score (XP score = −8.79) (Pg. 66, Col. 1, Para. 1 – Col. 2, Para. 1). Fourches et al. further teaches that the three poses with the highest random forest (RF) classifier scores were retained (see Table 2). The closer to 1 the consensus RF score predicted by the ensemble of pose-classifying models was, the higher was the probability that this pose was nativelike (i.e., according to the at least one feature value of each of the poses and the statistics distribution pair of each of the at least one feature, obtaining a score of each of the poses for each of the drugs so as to obtain a plurality of pose scores of each of the drugs) (Pg. 67, Table 2 and Pg. 67, Col. 2, Para. 1). Fourches et al. further teaches the predicted and experimental rankings for the 10 ligands in Table 3. The best correlation with experimental data for the 10 ligands was achieved by SP/RF method (poses created by Glide SP, filtered by RF/MCT-Tess models, and reranked on the basis of the Glide SP scoring function) (i.e., ranking the drugs according to the pose scores of each of the drugs) (Pg. 68, Table 3 and Pg. 68, Col. 1, Para. 4).
Regarding claims 7 and 15, Fourches et al. teaches the ranking of compounds with the most favorable docking scores (compound cs335) as 1 and the least favorable docking scores (compound cs333) as 10) (i.e., wherein the step (d) comprises: ranking the drugs in a high-to-low manner according to a highest score among the pose scores of each of the drugs) (Pg. 66, Table 1).
Therefore, Fourches et al. teaches all the limitations in claims 1, 7, 9, and 15.
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.
1. Claims 2-4 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Fourches et al. as applied to claims 1, 7, 9, and 15 above, and further in view of Wu et al. (COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Research. 46(W1): W438-442 (2018); published 5/28/2018).
Fourches et al, as applied to claims 1, 7, 9, and 15 above, does not teach wherein the at least one feature comprises a binding affinity, a distance between a pose and an assigned target residue, and a size of a pose cluster (claims 2 and 10); wherein the at least one feature comprises a ratio of a heavy atom of an assigned target residue of a protein encountered by a screened drug, wherein encountered by is defined as the heavy atom of the assigned target residue of the protein is in contact with the heavy atom of the screened drug within 4 Å (claims 3 and 11); and wherein the at least one feature comprises a distance between a pose and at least one active site residue (claims 4 and 12).
Regarding claims 2 and 10, Wu et al. teaches an improved docking algorithm to predict and identify protein-ligand binding sites (Abstract). Wu et al. further teaches that the output includes the top five protein–ligand binding pockets and the binding residues in each pocket as well as a summary of the top five predictions. The summary includes the rank, size of the cluster, predicted energy, and predicted binding residues (i.e., wherein the at least one feature comprises a binding affinity and a size of the pose cluster) (Pg. W439, Col. 2, Para. 1 and Pg. W440, Fig. 2).
Regarding claims 3 and 11, Wu et al. teaches that the ligand-binding residues were obtained based on atomic distance calculations with the protein-ligand complex structures (Pg. W440, Col. 2, Para. 1). Wu et al. further teaches the calculation of a clash score, steric clash to the ligand in the predicted complex structures. A residue is said to have steric clash to the ligand when the closest atomic distance between the residue’s and the ligand’s atoms is less than three quarters of the sum of their Van der Waals radii (Pg. W441, Col. 1, Para. 1). Though not explicitly taught by Wu et al., it would be obvious to one of ordinary skill in the art to calculate a ratio of a heavy atom of an assigned target residue of a protein encountered by a screened drug because Wu et al. teaches the calculation of distance-based metrics to determine predicted binding residues (Pg. W440, Col. 2, Para. 1 and Pg. W440, Fig. 2). Wu et al. does not disclose that the cut-off distance is 4 Å, but discloses the use of Van der Waals radii to determine clashes, and it would therefore be obvious to use distances on the same scale as the Van der Waals radii (e.g., 4 Å or less) as a measure for an "encountered" distance (i.e., wherein the at least one feature comprises a ratio of a heavy atom of an assigned target residue of a protein encountered by a screened drug, wherein encountered by is defined as the heavy atom of the assigned target residue of the protein is in contact with the heavy atom of the screened drug within 4 Å).
Regarding claims 4 and 12, Wu et al. teaches that the ligand-binding residues were obtained based on atomic distance calculations with the protein-ligand complex structures (i.e., wherein the at least one feature comprises a distance between a pose and at least one active site residue) (Pg. W440, Col. 2, Para. 1).
Therefore, regarding claims 2-4 and 10-12, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify docking and ranking method of Fourches et al. with the pose and binding site analysis of Wu et al. because the ligand-binding site prediction of Wu et al. significantly outperforms other ligand-binding site prediction methods by using a consensus of five individual methods (Wu et al., Pg. W438, Col. 2, Para. 3 and Pg. W442, Col. 1, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Fourches et al. with Wu et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for analyzing docking poses. Therefore, regarding claims 2-4 and 10-12, the instant invention is prima facie obvious (MPEP § 2142).
2. Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Fourches et al. as applied to claims 1, 7, 9, and 15 above, and further in view of Tsai et al. (DRDOCK: A Drug Repurposing platform integrating automated docking, simulations, and a log-odds-based drug ranking scheme. bioRxiv. Version 3, DOI: 10.1101/2021.01.31.429052; published 6/2/2021; cited in the IDS dated 1/11/2023).
Regarding claims 5 and 13, Fourches et al. teaches the frequency distribution of poses vs. RMSD (i.e., the feature value) for both native (true binders) and decoys in Fig. 2. The distribution is binned in intervals of 0.5 Å on the x-axis with the counts for each bin (i.e., frequency compared to the rest of the bins) on the y axis (i.e., according to a plurality of first feature values of the true binding poses corresponding to the current feature, in a plurality of first value intervals, calculating a first ratio of the true binding poses in each of the first value intervals to obtain a preliminary true binding pose statistics distribution corresponding to the current feature and according to a plurality of second feature values of the decoy poses corresponding to the current feature, in a plurality of second value intervals, calculating a second ratio of the decoy poses in each of the second value intervals to obtain a preliminary decoy pose statistics distribution corresponding to the current feature) (Pg. 65, Fig. 2). Fourches et al. further teaches the use of one feature (RMSD), and therefore the method does not need to be repeated (i.e., repeatedly executing the step (a1) until all the at least one feature is processed) (Pg. 65, Col. 1, Para. 1 and Pg. 65, Fig. 2).
Fourches et al., as applied to claims 1, 7, 9, and 15 above, does not teach fitting the preliminary true binding pose statistics distribution corresponding to the current feature by a first fitting function to obtain the true binding pose statistics distribution corresponding to the current feature; fitting the preliminary decoy pose statistics distribution corresponding to the current feature by a second fitting function to obtain the decoy pose statistics distribution corresponding to the current feature (claims 5 and 13); applying a current feature value corresponding to the current feature in the at least one feature value into the true binding pose statistics distribution corresponding to the current feature so as to obtain a first probability (claims 6 and 14); applying the current feature value corresponding to the current feature in the at least one feature value into the decoy pose statistics distribution corresponding to the current feature so as to obtain a second probability (claims 6 and 14); dividing the first probability by the second probability to obtain a value and deriving the logarithm of the value so as to obtain a current feature score corresponding to the current feature (claims 6 and 14); repeatedly executing the step (c1) until all the at least one feature is processed (claims 6 and 14; and after all the at least one feature is processed, summing up all the current features scores obtained from the steps (c1)-(c2) so as to obtain the score of the current pose (claims 6 and 14).
Regarding claims 5 and 13, Tsai et al. teaches a method for automatic screening of drugs by using a novel drug-ranking scheme incorporating log-odds scores derived from feature distributions of true binders and decoys (Abstract). Tsai et al. further teaches that to get the statistics distributions, they pooled all the generated poses from the 16 protein-drug complexes in the training set and derived the distribution of the true binding poses and decoys for each of the features associated with each pose (Pg. 8, Para. 2). Tsai et al. further teaches that they quantified the difference between the distributions by Kolmogorov–Smirnov (K-S) test and Kullback–Leibler (K-L) divergence. A feature is considered “preferred” if it gives a large K-S statistics or K-L divergence (the “distance” between two distributions) between the distribution of true binders and that of decoys. The two distributions for each of the features are shown in Figures 2 to 4 (i.e., fitting the preliminary true binding pose statistics distribution corresponding to the current feature by a first fitting function to obtain the true binding pose statistics distribution corresponding to the current feature; fitting the preliminary decoy pose statistics distribution corresponding to the current feature by a second fitting function to obtain the decoy pose statistics distribution corresponding to the current feature) (Pg. 15, Para. 4; Pg. 16, Fig. 2; Pg. 17, Fig. 3; and Pg. 18, Fig. 4).
Regarding claims 6 and 14, Tsai et al. teaches that the log-odds score for each of the sampled poses was calculated using all three of the following features: the pose affinity, the pose’s distance to the target site, and the size of poses cluster (i.e., executing following steps for a current feature of the at least one feature and repeatedly executing the step (c1) until all the at least one feature is processed) (Pg. 8, Para. 2). Tsai et al. further teaches that the distributions provided the statistical basis to annotate a sampled docking pose to be a true binder or decoy, based on its relative probability (Pg. 8, Para. 2). The log-odds (LOD) score (i.e., the current feature score) for each of the sampled poses is calculated according to the following equation:
L
O
D
s
c
o
r
e
=
∑
f
l
o
g
P
f
T
(
Q
f
x
)
P
f
F
(
Q
f
x
)
, where
x
represents a sampled docking pose,
f
represents one of the specified features,
Q
f
x
is the value of feature
f
for the pose
x
,
P
f
T
and
P
f
F
are the probability distributions of feature
f
for docking poses sampled from the true binders and the decoys respectively (i.e., the first probability and the second probability, respectively). Noted that
Q
f
x
are binned values and therefore different
Q
f
x
values within the same bin have the same
P
f
T
or
P
f
F
values (i.e., applying a current feature value corresponding to the current feature in the at least one feature value into the true binding pose statistics distribution corresponding to the current feature so as to obtain a first probability; applying the current feature value corresponding to the current feature in the at least one feature value into the decoy pose statistics distribution corresponding to the current feature so as to obtain a second probability; and dividing the first probability by the second probability to obtain a value and deriving the logarithm of the value so as to obtain a current feature score corresponding to the current feature) (Pg. 8, Para. 2). Tsai et al. further teaches that in the feature ranking score (FRS) method, the values for a single feature to all the poses of the drugs are sorted. For each pose, the ranking for each of the three features is summed to obtain an FRS score such that
F
R
S
=
∑
f
=
1
3
[
1
-
(
R
f
-
1
)
N
], where
f
represents one of the three features,
R
f
represents the ranking for a pose of feature
f
, and
N
is the total number of sampled poses for all the drugs. The pose with the highest FRS in each drug is used as the representative pose for that drug, which is then sorted to obtain the final ranking for the drugs (i.e., after all the at least one feature is processed, summing up all the current features scores obtained from steps (c1)-(c2) so as to obtain the score of the current pose) (Pg. 9, Para. 1).
Therefore, regarding claims 5-6 and 13-14, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify to modify docking and ranking method of Fourches et al. with the statistics distribution and scoring analysis of Tsai et al. because the use of the LOD score and the FRS based on both affinity and distance features (i.e., the method of Tsai et al.) provides the best ranking method to distinguish co-crystallized drugs from decoys among small molecule methods, enabling timely evaluation of compounds for drug repurposing (Tsai et al., Abstract and Pg. 19, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Fourches et al. with Tsai et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for analyzing docking poses. Therefore, regarding claims 5-6 and 13-14, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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