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Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges
Open accessAbstract
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.
Overview
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TL;DR
The paper reports mathematical deep learning models combining multiscale weighted colored subgraphs and element-specific persistent homology with machine learning for D3R Grand Challenge 2 and 3 pose, affinity ranking, and free energy tasks. In GC3, the authors’ submissions ranked 1st in 10 of 26 official competitive tasks, and in GC2 they achieved the top place in free energy prediction for free energy set 1 in stage 2.
Key findings
- In D3R GC3, the authors’ predictions were ranked 1st in 10 out of 26 official competitive tasks.
- For GC3 Subchallenge 1 (Cathepsin S), the best Kendall’s τ improved from 0.23 in stage 1 to 0.54 in stage 2 (receipt ID 6jekk); free energy RMSEc was 0.33 kcal/mol in stage 1 (receipt ID fomca) and 0.29 kcal/mol in stage 2 (receipt ID v4jv4).
- For GC3 Subchallenge 4 (TIE2), the model ranked first on all evaluated metrics: affinity ranking excluding Kds > 10 μM gave Kendall’s τ = 0.57 and Spearman = 0.76 (receipt ID uuihe); active/inactive classification gave MCC = 0.78; Set 1 absolute free energy gave MCC = 1.0 (receipt ID vwbp8); Set 2 gave RMSEc = 1.02 kcal/mol (receipt ID 5g8ed).
- In GC2, the submission with receipt ID 5bvwx ranked second in free energy set 1 of stage 1 with RMSEc = 0.68 kcal/mol, and in stage 2 the model obtained first place for free energy set 1 with Kendall’s τ = 0.41 (receipt ID 4rbjk).
Methods
- The models used multiscale weighted colored subgraph (MWCS) and element-specific persistent homology descriptors, combined with machine learning methods including random forest, gradient boosting trees, convolutional neural networks, and multi-task learning; training used PDBbind data.
- Ligands were prepared from SMILES using LIGPREP at pH 7.5, with Gasteiger charges added via MGLTools v1.5.6; docking used Autodock Vina, GOLD, GLIDE, induced fit docking, and several protocols named align-close, align-target, close-dock, cross-dock, constraint-IFD, and free-IFD.
- The paper evaluates submissions in D3R GC2 and GC3. GC2 involved FXR with 36 crystallographic ligands, 102 compounds for affinity ranking, and free energy subsets of 18 and 15 ligands. GC3 had five subchallenges involving Cathepsin S and kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, with 26 official competitive tasks; reported metrics include Kendall’s τ, Spearman correlation, RMSEc, and MCC.
Limitations
- The paper states that docking accuracy remains a bottleneck for affinity prediction, that none of the tried docking protocols showed a dominant role in binding affinity accuracy, and that excluding pose prediction error improved Kendall’s τ from 0.21 to 0.54 on Cathepsin S affinity ranking.
- The paper also states that machine learning-based scoring function performance depends crucially on training data quality and statistical distribution, and on selected features that may not accurately or completely describe protein–ligand binding interactions.
- Evident from the reported results: the models did not perform well on GC3 Subchallenge 2, with the best result being second place on VEGFR2 active/inactive classification (MCC = 0.48), and affinity ranking in Subchallenge 3 was described as unremarkable; GC2 pose ranking power was also described as not impressive.
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18 Own result · 14 Not checked yet · 5 Own interpretation · 5 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our submissions were ranked 1st in 10 of these 26 tasks as shown in Table 1 for additional information.”
- Also stated in: § Conclusion · plain
“In GC3, our submissions were ranked 1st in 10 out of 26 official evaluation tasks.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Conclusion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In stage 2, our models improved the accuracy of the energy prediction of compounds in the aforementioned free energy set.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 2
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We have tried a variety of docking protocols, namely align-close, align-target, close-dock, cross-dock, constraint-IFD, and free-IFD, for pose selection in GC3. However, none of them showed a dominant role in binding affinity accuracy.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Conclusion
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In the paper
“Specifically, on Set 1, our predictor with receipt ID vwbp8 was ranked the first place with MCC = 1.0.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Our predictions were ranked 1st in 10 out of these 26 tasks.”
- Also stated in: § Introduction · plain
“we obtained the first place in 10 out of a total of 26 predictive tasks.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Abstract
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- Also stated in: § Introduction · plain
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In the paper
“In addition, when one excludes the pose prediction error, Kendall’s tau of our model improves from 0.21 to 0.54 on the affinity ranking of compounds in Cathepsin S subchallenge.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Conclusion
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In the paper
“In fact, these additional docking features did not improve our available models.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our models achieved the top place in affinity ranking for the free energy set 1 in stage 2.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“We obtained the first place on the binding energy prediction with the centered RMSE as low as RMSEc = 1.06 kcal/mol (receipt ID 4u5ey).”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
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In the paper
“On Set 2, our model with receipt ID 5g8ed attained the RMSEc = 1.02 kcal/mol which is the lowest among all submissions.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
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In the paper
“When one is interested in active/inactive classification by including compounds having Kds > 10 μM, our model, receipt ID uuihe, is still ranked the first place with MCC = 0.78.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Our best submission has receipt ID rdn3k which achieves the best Kendall’s tau (τ = 0.52) for affinity ranking excluding Kds > 10 μM.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Specifically, for the affinity ranking excluding Kds > 10 μM, our model, receipt ID uuihe, produces the best Kendall’s τ and Spearman correlation coefficient among all of the participants with values being 0.57 and 0.76, respectively.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“We achieve the first place for all the evaluation metrics taken into account for this subchallenge.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Specifically, our submission with receipt ID 5bvwx was ranked the second place in the free energy set 1 of stage 1 with RMSEc = 0.68 kcal/mol.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 2
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, we still manage to sit at the second place on the active/inactive classification with Mathew correlation coefficient = 0.23 with receipt ID yqoad.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is worthy to mention that the docking accuracy is still a bottleneck of our affinity prediction performance.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Topological descriptors were further integrated with a variety of deep learning algorithms to achieve state-of-the-art analysis and prediction of protein folding stability change upon mutation [38], drug toxicity [39], aqueous solubility [40], partition coefficient [40], binding affinity [21, 22], and the virtual screening of active ligands and decoys [23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
What the sources say
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
- Not checked yetcites [22] Cang ZX, Wei GW (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13(7):e1005690. https://doi.org/10.1371/journal.pcbi.1005690 source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
None of the sources cited for this claim have been checked yet.
In the paper
“These fixed length features can be used with powerful machine learning methods such as the ensemble of trees and deep learning neural networks [21, 22].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
- Not checked yetcites [22] Cang ZX, Wei GW (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13(7):e1005690. https://doi.org/10.1371/journal.pcbi.1005690 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“We applied convolutional neural networks and multi-task learning in our predictions which further advanced the capability of our models [22, 23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Machine learning algorithms
What the sources say
- Not checked yetcites [22] Cang ZX, Wei GW (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13(7):e1005690. https://doi.org/10.1371/journal.pcbi.1005690 source
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: When the feature is complex or has an underlying dimension in feature space, deep learning models can further improve predictor performance.
None of the sources cited for this claim have been checked yet.
In the paper
“We have proposed element specific persistent homology, which is a family of persistent homology groups defined on various topological subspaces, to encode physical interactions into various topological invariants [21, 38].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
- Not checked yetcites [38] Cang ZX, Wei GW (2017) Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology. Bioinformatics 33:3549–3557
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By computing persistent homology on the subsets of the atoms, we can extract different chemical information.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
None of the sources cited for this claim have been checked yet.
In the paper
“Such a characterization can help machine learning-based SFs consistently maintain their accuracy in binding affinity predictions for a variety of protein–ligand complexes [19–23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
What the sources say
- Not checked yetcites [19] Li H, Leung K-S, Wong M-H, Ballester PJ (2014) Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: cyscore as a case study. BMC Bioinform 15(1):1 source
- Not checked yetcites [20] Nguyen DD, Xiao T, Wang ML, Wei GW (2017) Rigidity strengthening: a mechanism for protein–ligand binding. J Chem Inform Model 57:1715–1721 source
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
- Not checked yetcites [22] Cang ZX, Wei GW (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13(7):e1005690. https://doi.org/10.1371/journal.pcbi.1005690 source
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The performance of machine learning-based scoring functions depends crucially on the quality and statistical distribution of training data and on selected features that may or may not accurately and completely describe protein–ligand binding interactions.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Those results support that our mathematical deep learning models indeed gain a better performance when no pose prediction errors are involved.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For the characterization of small molecules, we can use a multilevel element specific persistent homology to both describe the covalent bonds and noncovalent interactions in the molecule [23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 9
- Probably not a substantial claim. 8
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“That explains why our predictors performed decently for scoring task in stage 1 with the best Kendall’s τ = 0.23, but they achieved a state-of-the-art result for the same task in stage 2 with the best Kendall’s τ = 0.54 (receipt ID 6jekk).”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, we obtained the first place in term of Kendall’s tau value (τ = 0.41) with receipt ID 4rbjk. That was also the highest Kendall’s tau value among all submissions in two stages for the free energy set 1.”
- Also stated in: § Conclusion · plain
“Overall, in GC2, our predictive models achieved the top place in free energy prediction for free energy set 1 in stage 2.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 2
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Also stated in: § Conclusion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Particularly, in stage 1, our prediction with receipt ID fomca obtained RMSEc = 0.33 kcal/mol. In stage 2, we accomplished RMSEc = 0.29 kcal/mol with receipt ID v4jv4.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our best result is the second place on the active/inactive classification of VERGFR2 set. Our best Matthews correlation coefficient (MCC) on such task is reported to be 0.48 from receipt ID rtv8m.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With this setting, our models attained the first places for the following tasks: free energy set in stage 1, scoring and free energy set in stage 2.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Results and discussion > Grand Challenge 3
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“The statistics of element specific persistent homology barcodes can also be used for featurization [23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Both our graph theory based models [20, 62] and algebraic topology based models [21, 23] achieve top-class performance with the ensemble of trees methods.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Machine learning algorithms
What the sources say
- Not checked yetcites [20] Nguyen DD, Xiao T, Wang ML, Wei GW (2017) Rigidity strengthening: a mechanism for protein–ligand binding. J Chem Inform Model 57:1715–1721 source
- Not checked yetcites [62] Nguyen DD, Wei GW (2018) Multiscale weighted colored algebraic graphs for biomolecules (to be submitted)
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, a higher level bagging of models of different kinds can further improve the performance.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Machine learning algorithms
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Random forest is better at lowering bias and gradient boosting trees focus more on reducing variance.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The topological analysis provides us a description of the molecular system that captures a collection of key aspects of the system including the multiscale description of geometry, the characterization of interaction network in an arbitrary dimension, and the important physical and chemical information, which ensures the success of the downstream machine learning modeling.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
None of the sources cited for this claim have been checked yet.
In the paper
“The barcodes can also be transformed to representations similar to images and used in a 1-dimensional or a 2-dimensional convolutional neural networks [22, 23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [22] Cang ZX, Wei GW (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13(7):e1005690. https://doi.org/10.1371/journal.pcbi.1005690 source
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“specific persistent homology computation on the collection of all carbon atoms describes the hydrophobic network or the structural basis of the molecule while computation on the nitrogen and oxygen atoms characterizes the hydrophilic networks [21].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [21] Cang ZX, Wei, GW (2018) “Integration of element specific persistent homology and machine learning for protein–ligand binding affinity prediction. Int J Numer Methods Biomed Eng. https://doi.org/10.1002/cnm.2914 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The MWCG approach has been shown to be over 40% more accurate than the Gaussian network model on a set of 364 proteins [24].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
What the sources say
- Not checked yetcites [24] Bramer D, Wei G-W (2018) Multiscale weighted colored graphs for protein flexibility and rigidity analysis. J Chem Phys 148(5):054103 source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
In the paper
“For example, Wasserstein metric can be applied to measure similarities among the barcodes of different proteins, which can be used with methods such as nearest neighbors and manifold learning [23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Methods > Algebraic topology based molecular signature
What the sources say
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Topological descriptors were further integrated with a variety of deep learning algorithms to achieve state-of-the-art analysis and prediction of protein folding stability change upon mutation [38], drug toxicity [39], aqueous solubility [40], partition coefficient [40], binding affinity [21, 22], and the virtual screening of active ligands and decoys [23].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
What the sources say
- Not checked yetcites [23] Cang ZX, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. https://doi.org/10.1371/journal.pcbi.1005929 source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results confirm the predictive power and practical usage of our mathematical deep learning models in drug design and discovery.”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“By using advanced machine learning algorithms, such as random forest (RF) and deep convolutional neural network, the machine learning-based SFs can characterize the non-additive contributions of functional groups in protein–ligand binding interactions [18].”
§ Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges > Introduction
What the sources say
- Not checked yetcites [18] Baum B, Muley L, Smolinski M, Heine A, Hangauer D, Klebe G (2010) Non-additivity of functional group contributions in protein–ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry. J Mol Biol 397(4):1042–1054 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The performance of machine learning-based scoring functions depends crucially on the quality and statistical distribution of training data and on selected features that may or may not accurately and completely describe protein–ligand binding interactions.
The authors state this as an assumption.
The intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.
“We assume that the intrinsic physics of interest of complex biomolecules and interactions lies on low-dimensional manifolds or subspaces embedded in a high-dimensional data space.”
The paper relies on this without support.
The performance of machine learning-based scoring functions depends crucially on the quality and statistical distribution of training data and on selected features that may or may not accurately and completely describe protein–ligand binding interactions.
“However, the performance of machine learning-based SFs depends crucially on the training data quality and statistic distribution. Additionally, it also depends on selected features that might or might not accurately and completely describe the protein–ligand binding interactions.”
The authors state this as an assumption.
Grouping weak learners can improve learning capability.
“Ensemble of trees methods build models consisting of a collection of decision trees with the assumption that grouping the weak learners can improve the learning capability.”
The paper relies on this without support.
Random forest is better at lowering bias and gradient boosting trees focus more on reducing variance.
“As these two models attempt to reduce error in two different ways, they behave differently in the bias-variance trade-off where the random forest is better at lowering bias and gradient boosting trees focus more on reducing variance.”
The paper relies on this without support.
Ensemble learning methods are robust and overfitting can be reduced by learning partial problems.
“The ensemble learning methods are also robust and overfitting can be reduced by learning partial problems.”
The paper relies on this without support.
When the feature is complex or has an underlying dimension in feature space, deep learning models can further improve predictor performance.
“When the feature is complex or there is some underlying dimension in the feature space, deep learning models can further imporve the performance of the predictor.”
The paper relies on this without support.
A spatial dimension associated to the filtration parameter lies in the persistent homology representation of protein–ligand systems.
“For example, a spatial dimension associated to the filtration parameter lies in the persistent homology representation of protein–ligand systems.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It has been devised to win worldwide competitions in computer-aided drug design36.”
From A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation (2020) · cites this paper as [36]
Citations
Cites
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AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 26,294
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A hierarchical approach to all‐atom protein loop prediction
Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T., Honig, B. H., Shaw, D. E., & Friesner, R. A. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10613 · Cited by 2,541
Research works printed but not linked: 2
- Schrödinger LLC (2017) Schrödinger release 2017-4. Schrödinger LLC, New York
- Edelsbrunner H (1992) Weighted alpha shapes. Technical report. University of Illinois, Champaign
Cited by
Sorted by citations
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 703
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
Deep learning methods for molecular representation and property prediction
Li, Z., Jiang, M., Wang, S., & Zhang, S. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103373 · Cited by 289
Machine and deep learning approaches for cancer drug repurposing
Issa, N. T., Stathias, V., Schürer, S. C., & Dakshanamurthy, S. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.12.011 · Cited by 292
Dhakal, A., McKay, C., Tanner, J. J., & Cheng, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab476 · Cited by 269
Metadata
- Authors
- Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., & Wei, G.-W.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Wei, Guo-Wei
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2018-08-16
- Volume
- 33
- Issue
- 1
- Pages
- 71-82
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- PMC
- PMC7163798
- OpenAlex
- W2963883198
- Semantic Scholar
- 2ca793d6b2ce88d315b20441f9aa4189965d429a
- Cited by
- 155
- References
- 70
- Citations by year
- 2026: 8 · 2025: 25 · 2024: 19 · 2023: 18 · 2022: 20 · 2021: 27 · 2020: 21 · 2019: 15 · 2018: 2
- FWCI
- 12.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1160352, 1302285, 1761320)
- National Institute of General Medical Sciences (R01GM126189)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- binding affinity ranking, free energy prediction, persistent homology, cathepsin S, kinase inhibitors, VEGFR2, JAK2, Tie2, c-Abl
- MeSH terms
- Deep Learning, Receptor, Farnesoid X-Activated, Binding Sites, Cathepsins, Ligands, Protein Binding, Protein Conformation, Protein Kinases, Thermodynamics, Drug Design, Computer-Aided Design, Receptors, Cytoplasmic and Nuclear, Crystallography, X-Ray, Databases, Protein, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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