Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Google Scholar
Muller, S. et al. Target 2035—update on the quest for a probe for every protein. RSC Med. Chem. 13, 13–21 (2022).
Google Scholar
Kaplan, A. L. et al. Bespoke library docking for 5-HT(2A) receptor agonists with antidepressant activity. Nature 610, 582–591 (2022).
Google Scholar
Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019).
Google Scholar
Shen, C. et al. Beware of the generic machine learning-based scoring functions in structure-based virtual screening. Brief. Bioinform. 22, bbaa070 (2021).
Google Scholar
Guedes, I. A., Pereira, F. S. S. & Dardenne, L. E. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front. Pharmacol. 9, 411637 (2018).
Google Scholar
Shen, C. et al. Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening? Brief. Bioinform. 22, bbaa410 (2021).
Google Scholar
Zhu, H., Yang, J. & Huang, N. Assessment of the generalization abilities of machine-learning scoring functions for structure-based virtual screening. J. Chem. Inf. Model. 62, 5485–5502 (2022).
Google Scholar
Francoeur, P. G. et al. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60, 4200–4215 (2020).
Google Scholar
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J. & Koes, D. R. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model. 57, 942–957 (2017).
Google Scholar
Li, S. et al. Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In Proc. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (eds Feida, Z. et al.) 975–985 (ACM, 2021); https://doi.org/10.1145/3447548.3467311
Lim, J. et al. Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation. J. Chem. Inf. Model. 59, 3981–3988 (2019).
Google Scholar
Moon, S., Zhung, W., Yang, S., Lim, J. & Kim, W. Y. PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chem. Sci. 13, 3661–3673 (2022).
Google Scholar
Shen, C. et al. Boosting protein-ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J. Med. Chem. 65, 10691–10706 (2022).
Google Scholar
Jiang, D. et al. InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions. J. Med. Chem. 64, 18209–18232 (2021).
Google Scholar
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E. A. & Wegner, J. K. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat. Mach. Intell. 3, 1033–1039 (2021).
Google Scholar
Li, Y. & Yang, J. Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein–ligand interactions. J. Chem. Inf. Model. 57, 1007–1012 (2017).
Google Scholar
Chen, L. et al. Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening. PLoS ONE 14, e0220113 (2019).
Google Scholar
Chatterjee, A. et al. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023).
Google Scholar
Geirhos, R. et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665–673 (2020).
Google Scholar
Sastry, G. M., Dixon, S. L. & Sherman, W. Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J. Chem. Inf. Model. 51, 2455–2466 (2011).
Google Scholar
Volkov, M. et al. On the frustration to predict binding affinities from protein–ligand structures with deep neural networks. J. Med. Chem. 65, 7946–7958 (2022).
Google Scholar
Li, S. et al. MONN: a multi-objective neural network for predicting compound-protein interactions and affinities. Cell Syst. 10, 308–322 (2020).
Google Scholar
Cain, S., Risheh, A. & Forouzesh, N. Calculation of protein-ligand binding free energy using a physics-guided neural network. In Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (eds Chen, Y. et al.) 2487–2493 (IEEE, 2021); https://doi.org/10.1109/bibm52615.2021.9669867
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R. & Jaakkola, T. Equibind: geometric deep learning for drug binding structure prediction. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 20503–20521 (PMLR, 2022); https://doi.org/10.48550/arXiv.2202.05146
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Google Scholar
Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023–1032 (2021).
Google Scholar
Thurlemann, M., Boselt, L. & Riniker, S. Learning atomic multipoles: prediction of the electrostatic potential with equivariant graph neural networks. J. Chem. Theory Comput. 18, 1701–1710 (2022).
Google Scholar
Batool, M., Ahmad, B. & Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20, 2783 (2019).
Google Scholar
Imrie, F., Bradley, A. R. & Deane, C. M. Generating property-matched decoy molecules using deep learning. Bioinformatics 37, 2134–2141 (2021).
Google Scholar
Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582–6594 (2012).
Google Scholar
Bauer, M. R., Ibrahim, T. M., Vogel, S. M. & Boeckler, F. M. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0—a public library of challenging docking benchmark sets. J. Chem. Inf. Model. 53, 1447–1462 (2013).
Google Scholar
Wang, L. et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc. 137, 2695–2703 (2015).
Google Scholar
Sieg, J., Flachsenberg, F. & Rarey, M. In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J. Chem. Inf. Model. 59, 947–961 (2019).
Google Scholar
Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).
Google Scholar
Adeshina, Y. O., Deeds, E. J. & Karanicolas, J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc. Natl Acad. Sci. USA 117, 18477–18488 (2020).
Google Scholar
Bouysset, C. & Fiorucci, S. ProLIF: a library to encode molecular interactions as fingerprints. J. Cheminform. 13, 72 (2021).
Google Scholar
Satorras, V. G., Hoogeboom, E. & Welling, M. E(n) equivariant graph neural networks. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 9323–9332 (PMLR, 2021); https://doi.org/10.48550/arXiv.2102.09844
Yun, S., Jeong, M., Kim, R., Kang, J. & Kim, H. J. Graph transformer networks. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 11983–11993 (NeurIPS, 2019); https://doi.org/10.48550/arXiv.1911.06455
Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).
Google Scholar
Mastropietro, A., Pasculli, G. & Bajorath, J. Learning characteristics of graph neural networks predicting protein–ligand affinities. Nat. Mach. Intell. 5, 1427–1436 (2023).
Google Scholar
Yu, Y., Lu, S., Gao, Z., Zheng, H. & Ke, G. Do deep learning models really outperform traditional approaches in molecular docking? Preprint at https://doi.org/10.48550/arXiv.2302.07134 (2023).
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221–234 (2013).
Google Scholar
Harder, E. et al. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281–296 (2016).
Google Scholar
Tuccinardi, T., Poli, G., Romboli, V., Giordano, A. & Martinelli, A. Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies. J. Chem. Inf. Model. 54, 2980–2986 (2014).
Google Scholar
Westbrook, J. D. et al. The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank. Bioinformatics 31, 1274–1278 (2015).
Google Scholar
UniProt, C. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).
Google Scholar
Wierbowski, S. D., Wingert, B. M., Zheng, J. & Camacho, C. J. Cross‐docking benchmark for automated pose and ranking prediction of ligand binding. Protein Sci. 29, 298–305 (2020).
Google Scholar
Shen, C. et al. The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction. J. Cheminform. 13, 1–18 (2021).
Google Scholar
Zhang, X. et al. TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions. J. Med. Chem. 65, 7918–7932 (2022).
Google Scholar
Su, M., Feng, G., Liu, Z., Li, Y. & Wang, R. Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set? J. Chem. Inf. Model. 60, 1122–1136 (2020).
Google Scholar
Scantlebury, J. et al. A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening. J. Chem. Inf. Model. 63, 2960–2974 (2023).
Google Scholar
Ying, C. et al. Do transformers really perform bad for graph representation? In Advances in Neural Information Processing Systems 34 (eds Ranzato, M. et al.) 28877–28888 (NeurIPS, 2021); https://doi.org/10.48550/arXiv.2106.05234
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1263–1272 (PMLR, 2017); https://doi.org/10.5555/3305381.3305512
Jiao, Q. et al. Edge-gated graph neural network for predicting protein-ligand binding affinities. In Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (eds Huang, Y. et al.) 334–339 (IEEE, 2021); https://doi.org/10.1109/bibm52615.2021.9669846
Shang, C. et al. Edge attention-based multi-relational graph convolutional networks. Preprint at https://doi.org/10.48550/arXiv.1802.04944 (2018).
Gong, L. & Cheng, Q. Exploiting edge features for graph neural networks. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (eds Michael S. B. et al.) 9203–9211 (IEEE, 2019); https://doi.org/10.1109/CVPR.2019.00943
Dwivedi, V. P. & Bresson, X. A generalization of transformer networks to graphs. Preprint at https://doi.org/10.48550/arXiv.2012.09699 (2020).
Bradley, A. P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 30, 1145–1159 (1997).
Google Scholar
Xue, Y., Tong, Y. & Neri, F. An ensemble of differential evolution and Adam for training feed-forward neural networks. Inf. Sci. 608, 453–471 (2022).
Google Scholar
Lu, W. et al. Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction. In Advances in Neural Information Processing Systems 35 (eds Koyejo, S. et al.) 7236–7249 (NeurIPS, 2022); https://doi.org/10.1101/2022.06.06.495043
Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).
Google Scholar
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).
Google Scholar
Irwin, J. J. & Shoichet, B. K. ZINC—a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182 (2005).
Google Scholar
Truchon, J. F. & Bayly, C. I. Evaluating virtual screening methods: good and bad metrics for the ‘early recognition’ problem. J. Chem. Inf. Model. 47, 488–508 (2007).
Google Scholar
Cao, D., Chen, G., Jiang, J. & Zheng, M. PDBscreen with multiple data augmentation strategies suitable for training protein-ligand interaction prediction methods. Zenodo https://doi.org/10.5281/zenodo.8049380 (2023).
Cao, D., Chen, G., Jiang, J., Yu, J. & Zheng, M. TEST dataset pocket for EquiScore. Zenodo https://doi.org/10.5281/zenodo.8047224 (2023).
Cao, D. & Chen, G. Original data and supplementary information for ‘EquiScore is a generic protein–ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling’. Zenodo https://doi.org/10.5281/zenodo.10812637 (2023).
Cao, D. Code for ‘EquiScore is a generic protein–ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling’. GitHub https://github.com/CAODH/EquiScore (2023).
Cao, D. Code for ‘EquiScore is a generic protein–ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling’. Zenodo https://doi.org/10.5281/zenodo.10812534 (2023).
