The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index; none of the 26 sources its claims cite is checked for support yet.
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
AI snapshot
Written from the paper’s full text.
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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.
- 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.
- 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.
- 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.
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
“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
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.
- Based: passes — The paper gives a reason why it holds.
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
“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.
- 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.
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 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
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.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · 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 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
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 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
“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
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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
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
“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
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.
- Based: passes — The paper gives a reason why it holds.
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
“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
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.
- Based: passes — The paper gives a reason why it holds.
- 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.
- 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 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.
- 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.
- 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.
- 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 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.
- Based: passes — The paper gives a reason why it holds.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- Based: passes — The paper gives a reason why it holds.
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
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
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
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Open Babel: An open chemical toolbox
O'Boyle, N. M., Banck, M. S., James, C. A. J., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-33 · Cited by 10,745
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9644-8 · Cited by 6,207
Borgatti, S. P. (2005) · Social Networks · doi:10.1016/j.socnet.2004.11.008 · Cited by 3,191
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
Nguyen, D. D., & Wei, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00334 · Cited by 224
Self-citationWang, M., Cang, Z., & Wei, G.-W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0149-6 · Cited by 202
Self-citationMeng, Z., & Xia, K. (2021) · Science Advances · doi:10.1126/sciadv.abc5329 · Cited by 175
Machine‐learning scoring functions for structure‐based drug lead optimization
Li, H., Sze, K., Lü, G., & Ballester, P. J. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1465 · Cited by 150
Parks, C. D., Gaieb, Z., Chiu, M. W., Yang, H., Shao, C., Walters, W. P., Jansen, J. M., McGaughey, G. B., Lewis, R. A., Bembenek, S. D., et al. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00289-y · Cited by 140
Are 2D fingerprints still valuable for drug discovery?
Gao, K., Nguyen, D. D., Sresht, V., Mathiowetz, A. M., Tu, M., & Wei, G. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp00305k · Cited by 136
Self-citationPersistent-homology-based machine learning: a survey and a comparative study
Pun, C. S., Lee, S. X., & Xia, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10146-z · Cited by 244
A review on compound-protein interaction prediction methods: Data, format, representation and model
Lim, S., Lu, Y., Cho, C. Y., Sung, I., Kim, J.-W., Kim, Y., Park, S., & Kim, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.004 · Cited by 120
Lu, J., Hou, X., Wang, C., & Zhang, Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00645 · Cited by 116
Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications
Dhudum, R., Ganeshpurkar, A., & Pawar, A. (2024) · Drugs and Drug Candidates · doi:10.3390/ddc3010009 · Cited by 131
Wang, R., Nguyen, D. D., & Wei, G. (2020) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3376 · Cited by 112
Self-citationMathDL: mathematical deep learning for D3R Grand Challenge 4
Nguyen, D. D., Gao, K., Wang, M., & Wei, G. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00237-5 · Cited by 105
Self-citationA practical guide to machine-learning scoring for structure-based virtual screening
Tran‐Nguyen, V., Junaid, M., Simeon, S., & Ballester, P. J. (2023) · Nature Protocols · doi:10.1038/s41596-023-00885-w · Cited by 106
Morrone, J. A., Weber, J. K., Huynh, T., Luo, H., & Cornell, W. D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00927 · Cited by 97
A review of mathematical representations of biomolecular data
Nguyen, D. D., Cang, Z., & Wei, G. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp06554g · Cited by 101
Self-citationRNA –ligand molecular docking: Advances and challenges
Zhou, Y., Jiang, Y., & Chen, S. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1571 · Cited by 94
Persistent-Homology-Based Machine Learning and Its Applications -- A SurveyPreprint
Pun, C. S., Xia, K., & Lee, S. X. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3275996 · Cited by 89
Qiu, Y., & Wei, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad289 · Cited by 90
Self-citationMethodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Gao, K., Wang, R., Chen, J., Cheng, L., Frishcosy, J., Huzumi, Y., Qiu, Y., Schluckbier, T., Wei, X., & Wei, G. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00965 · Cited by 93
Self-citationTopological methods for data modelling
Carlsson, G. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-00249-3 · Cited by 85
Emerging Vaccine-Breakthrough SARS-CoV-2 Variants
Wang, R., Chen, J., Hozumi, Y., Yin, C., & Wei, G. (2022) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.1c00557 · Cited by 82
Self-citationA review of machine learning methods for imbalanced data challenges in chemistry
Jian, J., Zhang, C., Ke, L., Hayes, N., Zhu, Y., Qiu, H., Zhang, B., Zhou, T., & Wei, G. (2025) · Chemical Science · doi:10.1039/d5sc00270b · Cited by 76
Self-citationGGL-Tox: Geometric Graph Learning for Toxicity Prediction
Jiang, J., Wang, R., & Wei, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01294 · Cited by 76
Self-citationYang, C., & Zhang, Y. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00485 · Cited by 74
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
Chen, J., Qiu, Y., Wang, R., & Wei, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106262 · Cited by 70
Self-citationDeltaDelta neural networks for lead optimization of small molecule potency
Jiménez-Luna, J., Pérez‐Benito, L., Martínez-Rosell, G., Sciabola, S., Torella, R., Tresadern, G., & Fabritiis, G. D. (2019) · Chemical Science · doi:10.1039/c9sc04606b · Cited by 73
Weighted persistent homology for biomolecular data analysis
Meng, Z., Anand, D. V., Lu, Y., Wu, J., & Xia, K. (2020) · Scientific Reports · doi:10.1038/s41598-019-55660-3 · Cited by 74
Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges
Qi, X., Zhao, Y., Qi, Z., Hou, S., & Chen, J. (2024) · Molecules · doi:10.3390/molecules29040903 · Cited by 75
Ghislat, G., Rahman, T., & Ballester, P. J. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.009 · Cited by 65
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design
Li, X., Liu, X., Lü, L., Hua, X., Chi, Y., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac231 · Cited by 65
Liu, X., Feng, H., Wu, J., & Xia, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab127 · Cited by 65
SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction
Zhang, S., Jin, Y., Liu, T., Wang, Q., Zhang, Z., Zhao, S., & Shan, B. (2023) · ACS Omega · doi:10.1021/acsomega.3c00085 · Cited by 67
Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design
Liu, X., Wang, X., Wu, J., & Xia, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa411 · Cited by 60
Potentially highly potent drugs for 2019-nCoVPreprint
Nguyen, D. D., Gao, K., Chen, J., Wang, R., & Wei, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.05.936013 · Cited by 62
Self-citationRNAPosers: Machine Learning Classifiers for Ribonucleic Acid–Ligand Poses
Chhabra, S., Xie, J., & Frank, A. T. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c02322 · Cited by 57
Wee, J., & Xia, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab136 · Cited by 57
Machine intelligence design of 2019-nCoV drugsPreprint
Gao, K., Nguyen, D. D., Wang, R., & Wei, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.30.927889 · Cited by 56
Self-citationFresnais, L., & Ballester, P. J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa095 · Cited by 52
Evolutionary de Rham-Hodge method
Chen, J., Zhao, R., Tong, Y., & Wei, G. (2020) · Discrete and Continuous Dynamical Systems - B · doi:10.3934/dcdsb.2020257 · Cited by 52
Self-citationXiong, G., Shen, C., Yang, Z., Jiang, D., Liu, S., Lü, A., Chen, X., Hou, T., & Cao, D. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1567 · Cited by 47
Ghislat, G., Hernández-Hernández, S., Piyawajanusorn, C., & Ballester, P. J. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2403639 · Cited by 53
Wee, J., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac024 · Cited by 48
Topology-Based Machine Learning Strategy for Cluster Structure Prediction
Chen, X., Chen, D., Weng, M., Jiang, Y., Wei, G., & Pan, F. M. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00974 · Cited by 46
Self-citationAn introduction to multiparameter persistence
Botnan, M. B., & Lesnick, M. (2023) · EMS series of congress reports · doi:10.4171/ecr/19/4 · Cited by 45
Zhu, H., Yang, J., & Huang, N. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01149 · Cited by 44
Machine learning small molecule properties in drug discovery
Schapin, N., Majewski, M., Varela‐Rial, A., Arróniz, C., & Fabritiis, G. D. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100020 · Cited by 44
Structure-based protein–ligand interaction fingerprints for binding affinity prediction
Wang, D. D., Chan, M.-T., & Yan, H. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.018 · Cited by 42
Li, Z., Huang, R., Xia, M., Patterson, T. A., & Hong, H. (2024) · Biomolecules · doi:10.3390/biom14010072 · Cited by 41
Anand, D. V., Meng, Z., Xia, K., & Mu, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-66710-6 · Cited by 41
Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
Liu, X., Feng, H., Wu, J., & Xia, K. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009943 · Cited by 42
Lin_F9: A Linear Empirical Scoring Function for Protein–Ligand Docking
Yang, C., & Zhang, Y. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00737 · Cited by 33
Drug repositioning for cancer in the era of AI, big omics, and real-world data
Wieder, R., & Adam, N. R. (2022) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2022.103730 · Cited by 39
HERMES: Persistent spectral graph software
Wang, R., Zhao, R., Ribando-Gros, E., Chen, J., Tong, Y., & Wei, G. (2021) · Foundations of Data Science · doi:10.3934/fods.2021006 · Cited by 30
Self-citationMoshawih, S., Goh, H. P., Kifli, N., Idris, A. C., Yassin, H., Kotra, V., Goh, K. W., Liew, K. B., & Ming, L. C. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14062 · Cited by 32
Persistent Dirac for molecular representation
Wee, J., Bianconi, G., & Xia, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-37853-z · Cited by 33
A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences
Wee, J., & Jiang, J. W. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02266 · Cited by 36
Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints
Leidner, F., Yılmaz, N. K., & Schiffer, C. A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00457 · Cited by 31
Persistent Topological Laplacians—A Survey
Wei, X., & Wei, G. (2025) · Mathematics · doi:10.3390/math13020208 · Cited by 28
Self-citationJiang, P., Chi, Y., Li, X., Meng, Z., Liu, X., Hua, X., & Xia, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab527 · Cited by 27
Wang, R., & Wei, G. (2022) · Foundations of Data Science · doi:10.3934/fods.2022015 · Cited by 26
Self-citationWei, X., & Wei, G. (2024) · Foundations of Data Science · doi:10.3934/fods.2024033 · Cited by 29
Self-citationBieniek, M., Bhati, A. P., Wan, S., & Coveney, P. V. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01179 · Cited by 25
Generative network complex (GNC) for drug discovery
Grow, C., Gao, K., Nguyen, D. D., & Wei, G. (2019) · Communications in Information and Systems · doi:10.4310/cis.2019.v19.n3.a2 · Cited by 23
Self-citationApplications of Machine Learning in Knowledge Management System: A Comprehensive Review
Simon, C. G. K., Jhanjhi, N. Z., Goh, W. W., & Sukumaran, S. (2022) · Journal of Information & Knowledge Management · doi:10.1142/s0219649222500174 · Cited by 22
Persistent Path-Spectral (PPS) Based Machine Learning for Protein–Ligand Binding Affinity Prediction
Liu, R., Liu, X., & Wu, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01251 · Cited by 24
Gómez-Sacristán, P., Simeon, S., Tran‐Nguyen, V., Patil, S. P., & Ballester, P. J. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.01.024 · Cited by 24
Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 28
Persistent Cohomology for Data With Multicomponent Heterogeneous Information
Cang, Z., & Wei, G. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1272226 · Cited by 27
Self-citationBoyles, F., Deane, C. M., & Morris, G. M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00096 · Cited by 24
Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking
Agarwal, R., Rajitha Rajeshwar, T., & Smith, J. C. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01533 · Cited by 24
Topology of Folded Molecular Chains: From Single Biomolecules to Engineered Origami
Scalvini, B., Sheikhhassani, V., Woodard, J. C., Aupič, J., Dame, R. T., Jerala, R., & Mashaghi, A. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.04.009 · Cited by 21
Data considerations for predictive modeling applied to the discovery of bioactive natural products
Xue, H. T., Stanley-Baker, M., Kong, A. W., Li, H., & Goh, W. W. B. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.009 · Cited by 22
Rana, M. M., & Nguyen, D. D. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107250 · Cited by 21
Self-citationTopological data analysis and topological deep learning beyond persistent homology: a review
Su, Z., Liu, X., Hamdan, L. B., Maroulas, V., Wu, J., Carlsson, G., & Wei, G.-W. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11462-w · Cited by 38
Self-citationLiu, X., Feng, H., Wu, J., & Xia, K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00580 · Cited by 19
Wee, J., Chen, J., Xia, K., & Wei, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107918 · Cited by 20
Self-citationPersistent homology analysis of osmolyte molecular aggregation and their hydrogen-bonding networks
Xia, K., Anand, D. V., Saxena, S., & Mu, Y. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03009c · Cited by 17
Knot data analysis using multiscale Gauss link integral
Shen, L., Feng, H., Li, F., Lei, F., Wu, J., & Wei, G. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408431121 · Cited by 21
Self-citationTopological machine learning for multivariate time series
Wu, C., & Hargreaves, C. A. (2021) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2021.1871971 · Cited by 17
Computational representations of protein–ligand interfaces for structure-based virtual screening
Tong, Q., Zhu, Z., Wang, X. S., Xia, J., & Wu, S. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1929921 · Cited by 18
Dong, L., Shi, S., Qu, X., Luo, D., & Wang, B. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp03651k · Cited by 18
Houshmand, F., & Houshmand, S. (2023) · Frontiers in Health Informatics · doi:10.30699/fhi.v12i0.478 · Cited by 18
Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4
Kotelnikov, S., Alekseenko, A. E., Liu, C., Ignatov, M., Padhorny, D., Brini, E., Lukin, M., Coutsias, E. A., Dill, K. A., & Kozakov, D. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00257-1 · Cited by 14
MLIMC: Machine learning-based implicit-solvent Monte Carlo
Chen, J., Geng, W., & Wei, G. (2021) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/cjcp2109150 · Cited by 14
Self-citationMayer-Homology Learning Prediction of Protein-Ligand Binding Affinities
Feng, H., Shen, L., Liu, J., & Wei, G.-W. (2024) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524500613 · Cited by 14
Self-citationCategory-specific topological learning of metal–organic frameworks
Chen, D., Chen, C., & Wei, G.-W. (2025) · Journal of Materials Chemistry A · doi:10.1039/d4ta08877h · Cited by 14
Self-citationExploring fragment-based target-specific ranking protocol with machine learning on cathepsin S
Yang, Y., Lu, J., Yang, C., & Zhang, Y. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00247-3 · Cited by 11
Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions
Tran‐Nguyen, V., Simeon, S., Junaid, M., & Ballester, P. J. (2022) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2022.06.002 · Cited by 12
Huang, S., & Cao, Y. (2019) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2019.8222 · Cited by 12
Self-citationDocking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4
Kadukova, M., Chupin, V., & Grudinin, S. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00263-3 · Cited by 12
Feng, H., Suwayyid, F., Zia, M., Wee, J., Hozumi, Y., Chen, C., & Wei, G. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00940 · Cited by 10
Self-citationNeighborhood Complex Based Machine Learning (NCML) Models for Drug Design
Liu, X., & Xia, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87444-5_9 · Cited by 10
Persistent Tor-algebra for protein–protein interaction analysis
Liu, X., Feng, H., Lü, Z., & Xia, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad046 · Cited by 11
Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit
Mucllari, E., Zadorozhnyy, V., Ye, Q., & Nguyen, D. D. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01526 · Cited by 10
Self-citationBansal, N., Wang, Y., & Sciabola, S. (2024) · Molecules · doi:10.3390/molecules29040830 · Cited by 10
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 ›
Are you an author, or do you see a problem with this page? Request a review
