{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T08:19:30Z","timestamp":1782634770176,"version":"3.54.5"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-024-00849-z","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T10:03:08Z","timestamp":1717668188000},"page":"688-700","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Generic protein\u2013ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling"],"prefix":"10.1038","volume":"6","author":[{"given":"Duanhua","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-7649","authenticated-orcid":false,"given":"Jie","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runze","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3007-7215","authenticated-orcid":false,"given":"Lifan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feisheng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenghao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-0643","authenticated-orcid":false,"given":"Xutong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-3417","authenticated-orcid":false,"given":"Xiaomin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9167-4689","authenticated-orcid":false,"given":"Sulin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3323-3092","authenticated-orcid":false,"given":"Mingyue","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"849_CR1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).","journal-title":"Nature"},{"key":"849_CR2","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","volume":"373","author":"M Baek","year":"2021","unstructured":"Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871\u2013876 (2021).","journal-title":"Science"},{"key":"849_CR3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1039\/D1MD00228G","volume":"13","author":"S Muller","year":"2022","unstructured":"Muller, S. et al. Target 2035\u2014update on the quest for a probe for every protein. RSC Med. Chem. 13, 13\u201321 (2022).","journal-title":"RSC Med. Chem."},{"key":"849_CR4","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1038\/s41586-022-05258-z","volume":"610","author":"AL Kaplan","year":"2022","unstructured":"Kaplan, A. L. et al. Bespoke library docking for 5-HT(2A) receptor agonists with antidepressant activity. Nature 610, 582\u2013591 (2022).","journal-title":"Nature"},{"key":"849_CR5","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","volume":"566","author":"J Lyu","year":"2019","unstructured":"Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224\u2013229 (2019).","journal-title":"Nature"},{"key":"849_CR6","volume":"22","author":"C Shen","year":"2021","unstructured":"Shen, C. et al. Beware of the generic machine learning-based scoring functions in structure-based virtual screening. Brief. Bioinform. 22, bbaa070 (2021).","journal-title":"Brief. Bioinform."},{"key":"849_CR7","doi-asserted-by":"crossref","first-page":"411637","DOI":"10.3389\/fphar.2018.01089","volume":"9","author":"IA Guedes","year":"2018","unstructured":"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).","journal-title":"Front. Pharmacol."},{"key":"849_CR8","volume":"22","author":"C Shen","year":"2021","unstructured":"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).","journal-title":"Brief. Bioinform."},{"key":"849_CR9","doi-asserted-by":"crossref","first-page":"5485","DOI":"10.1021\/acs.jcim.2c01149","volume":"62","author":"H Zhu","year":"2022","unstructured":"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\u20135502 (2022).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR10","doi-asserted-by":"crossref","first-page":"4200","DOI":"10.1021\/acs.jcim.0c00411","volume":"60","author":"PG Francoeur","year":"2020","unstructured":"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\u20134215 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR11","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1021\/acs.jcim.6b00740","volume":"57","author":"M Ragoza","year":"2017","unstructured":"Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J. & Koes, D. R. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model. 57, 942\u2013957 (2017).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR12","doi-asserted-by":"publisher","unstructured":"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\u2013985 (ACM, 2021); https:\/\/doi.org\/10.1145\/3447548.3467311","DOI":"10.1145\/3447548.3467311"},{"key":"849_CR13","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","volume":"59","author":"J Lim","year":"2019","unstructured":"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\u20133988 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR14","doi-asserted-by":"crossref","first-page":"3661","DOI":"10.1039\/D1SC06946B","volume":"13","author":"S Moon","year":"2022","unstructured":"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\u20133673 (2022).","journal-title":"Chem. Sci."},{"key":"849_CR15","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.1021\/acs.jmedchem.2c00991","volume":"65","author":"C Shen","year":"2022","unstructured":"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\u201310706 (2022).","journal-title":"J. Med. Chem."},{"key":"849_CR16","doi-asserted-by":"crossref","first-page":"18209","DOI":"10.1021\/acs.jmedchem.1c01830","volume":"64","author":"D Jiang","year":"2021","unstructured":"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\u201318232 (2021).","journal-title":"J. Med. Chem."},{"key":"849_CR17","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1038\/s42256-021-00409-9","volume":"3","author":"O M\u00e9ndez-Lucio","year":"2021","unstructured":"M\u00e9ndez-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\u20131039 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"849_CR18","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1021\/acs.jcim.7b00049","volume":"57","author":"Y Li","year":"2017","unstructured":"Li, Y. & Yang, J. Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein\u2013ligand interactions. J. Chem. Inf. Model. 57, 1007\u20131012 (2017).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR19","doi-asserted-by":"crossref","first-page":"e0220113","DOI":"10.1371\/journal.pone.0220113","volume":"14","author":"L Chen","year":"2019","unstructured":"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).","journal-title":"PLoS ONE"},{"key":"849_CR20","volume":"14","author":"A Chatterjee","year":"2023","unstructured":"Chatterjee, A. et al. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023).","journal-title":"Nat. Commun."},{"key":"849_CR21","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R. et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665\u2013673 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"849_CR22","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1021\/ci2002704","volume":"51","author":"GM Sastry","year":"2011","unstructured":"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\u20132466 (2011).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR23","doi-asserted-by":"crossref","first-page":"7946","DOI":"10.1021\/acs.jmedchem.2c00487","volume":"65","author":"M Volkov","year":"2022","unstructured":"Volkov, M. et al. On the frustration to predict binding affinities from protein\u2013ligand structures with deep neural networks. J. Med. Chem. 65, 7946\u20137958 (2022).","journal-title":"J. Med. Chem."},{"key":"849_CR24","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.cels.2020.03.002","volume":"10","author":"S Li","year":"2020","unstructured":"Li, S. et al. MONN: a multi-objective neural network for predicting compound-protein interactions and affinities. Cell Syst. 10, 308\u2013322 (2020).","journal-title":"Cell Syst."},{"key":"849_CR25","doi-asserted-by":"publisher","unstructured":"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\u20132493 (IEEE, 2021); https:\/\/doi.org\/10.1109\/bibm52615.2021.9669867","DOI":"10.1109\/bibm52615.2021.9669867"},{"key":"849_CR26","doi-asserted-by":"publisher","unstructured":"St\u00e4rk, 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\u201320521 (PMLR, 2022); https:\/\/doi.org\/10.48550\/arXiv.2202.05146","DOI":"10.48550\/arXiv.2202.05146"},{"key":"849_CR27","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-022-29939-5","volume":"13","author":"S Batzner","year":"2022","unstructured":"Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).","journal-title":"Nat. Commun."},{"key":"849_CR28","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/s42256-021-00418-8","volume":"3","author":"K Atz","year":"2021","unstructured":"Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023\u20131032 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"849_CR29","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1021\/acs.jctc.1c01021","volume":"18","author":"M Thurlemann","year":"2022","unstructured":"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\u20131710 (2022).","journal-title":"J. Chem. Theory Comput."},{"key":"849_CR30","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.3390\/ijms20112783","volume":"20","author":"M Batool","year":"2019","unstructured":"Batool, M., Ahmad, B. & Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20, 2783 (2019).","journal-title":"Int. J. Mol. Sci."},{"key":"849_CR31","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.1093\/bioinformatics\/btab080","volume":"37","author":"F Imrie","year":"2021","unstructured":"Imrie, F., Bradley, A. R. & Deane, C. M. Generating property-matched decoy molecules using deep learning. Bioinformatics 37, 2134\u20132141 (2021).","journal-title":"Bioinformatics"},{"key":"849_CR32","doi-asserted-by":"crossref","first-page":"6582","DOI":"10.1021\/jm300687e","volume":"55","author":"MM Mysinger","year":"2012","unstructured":"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\u20136594 (2012).","journal-title":"J. Med. Chem."},{"key":"849_CR33","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1021\/ci400115b","volume":"53","author":"MR Bauer","year":"2013","unstructured":"Bauer, M. R., Ibrahim, T. M., Vogel, S. M. & Boeckler, F. M. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0\u2014a public library of challenging docking benchmark sets. J. Chem. Inf. Model. 53, 1447\u20131462 (2013).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR34","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1021\/ja512751q","volume":"137","author":"L Wang","year":"2015","unstructured":"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\u20132703 (2015).","journal-title":"J. Am. Chem. Soc."},{"key":"849_CR35","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1021\/acs.jcim.8b00712","volume":"59","author":"J Sieg","year":"2019","unstructured":"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\u2013961 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR36","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235\u2013242 (2000).","journal-title":"Nucleic Acids Res."},{"key":"849_CR37","doi-asserted-by":"crossref","first-page":"18477","DOI":"10.1073\/pnas.2000585117","volume":"117","author":"YO Adeshina","year":"2020","unstructured":"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\u201318488 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"849_CR38","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13321-021-00548-6","volume":"13","author":"C Bouysset","year":"2021","unstructured":"Bouysset, C. & Fiorucci, S. ProLIF: a library to encode molecular interactions as fingerprints. J. Cheminform. 13, 72 (2021).","journal-title":"J. Cheminform."},{"key":"849_CR39","doi-asserted-by":"publisher","unstructured":"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\u20139332 (PMLR, 2021); https:\/\/doi.org\/10.48550\/arXiv.2102.09844","DOI":"10.48550\/arXiv.2102.09844"},{"key":"849_CR40","doi-asserted-by":"publisher","unstructured":"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\u201311993 (NeurIPS, 2019); https:\/\/doi.org\/10.48550\/arXiv.1911.06455","DOI":"10.48550\/arXiv.1911.06455"},{"key":"849_CR41","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1021\/jm0306430","volume":"47","author":"RA Friesner","year":"2004","unstructured":"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\u20131749 (2004).","journal-title":"J. Med. Chem."},{"key":"849_CR42","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1038\/s42256-023-00756-9","volume":"5","author":"A Mastropietro","year":"2023","unstructured":"Mastropietro, A., Pasculli, G. & Bajorath, J. Learning characteristics of graph neural networks predicting protein\u2013ligand affinities. Nat. Mach. Intell. 5, 1427\u20131436 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"849_CR43","doi-asserted-by":"publisher","unstructured":"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).","DOI":"10.48550\/arXiv.2302.07134"},{"key":"849_CR44","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10822-013-9644-8","volume":"27","author":"GM Sastry","year":"2013","unstructured":"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\u2013234 (2013).","journal-title":"J. Comput. Aided Mol. Des."},{"key":"849_CR45","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1021\/acs.jctc.5b00864","volume":"12","author":"E Harder","year":"2016","unstructured":"Harder, E. et al. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281\u2013296 (2016).","journal-title":"J. Chem. Theory Comput."},{"key":"849_CR46","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1021\/ci500424n","volume":"54","author":"T Tuccinardi","year":"2014","unstructured":"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\u20132986 (2014).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR47","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1093\/bioinformatics\/btu789","volume":"31","author":"JD Westbrook","year":"2015","unstructured":"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\u20131278 (2015).","journal-title":"Bioinformatics"},{"key":"849_CR48","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","volume":"49","author":"C UniProt","year":"2021","unstructured":"UniProt, C. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480\u2013D489 (2021).","journal-title":"Nucleic Acids Res."},{"key":"849_CR49","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1002\/pro.3784","volume":"29","author":"SD Wierbowski","year":"2020","unstructured":"Wierbowski, S. D., Wingert, B. M., Zheng, J. & Camacho, C. J. Cross\u2010docking benchmark for automated pose and ranking prediction of ligand binding. Protein Sci. 29, 298\u2013305 (2020).","journal-title":"Protein Sci."},{"key":"849_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-021-00560-w","volume":"13","author":"C Shen","year":"2021","unstructured":"Shen, C. et al. The impact of cross-docked poses on performance of machine learning classifier for protein\u2013ligand binding pose prediction. J. Cheminform. 13, 1\u201318 (2021).","journal-title":"J. Cheminform."},{"key":"849_CR51","doi-asserted-by":"crossref","first-page":"7918","DOI":"10.1021\/acs.jmedchem.2c00460","volume":"65","author":"X Zhang","year":"2022","unstructured":"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\u20137932 (2022).","journal-title":"J. Med. Chem."},{"key":"849_CR52","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1021\/acs.jcim.9b00714","volume":"60","author":"M Su","year":"2020","unstructured":"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\u20131136 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR53","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1021\/acs.jcim.3c00322","volume":"63","author":"J Scantlebury","year":"2023","unstructured":"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\u20132974 (2023).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR54","doi-asserted-by":"publisher","unstructured":"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\u201328888 (NeurIPS, 2021); https:\/\/doi.org\/10.48550\/arXiv.2106.05234","DOI":"10.48550\/arXiv.2106.05234"},{"key":"849_CR55","doi-asserted-by":"publisher","unstructured":"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\u20131272 (PMLR, 2017); https:\/\/doi.org\/10.5555\/3305381.3305512","DOI":"10.5555\/3305381.3305512"},{"key":"849_CR56","doi-asserted-by":"publisher","unstructured":"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\u2013339 (IEEE, 2021); https:\/\/doi.org\/10.1109\/bibm52615.2021.9669846","DOI":"10.1109\/bibm52615.2021.9669846"},{"key":"849_CR57","doi-asserted-by":"publisher","unstructured":"Shang, C. et al. Edge attention-based multi-relational graph convolutional networks. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1802.04944 (2018).","DOI":"10.48550\/arXiv.1802.04944"},{"key":"849_CR58","doi-asserted-by":"publisher","unstructured":"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\u20139211 (IEEE, 2019); https:\/\/doi.org\/10.1109\/CVPR.2019.00943","DOI":"10.1109\/CVPR.2019.00943"},{"key":"849_CR59","doi-asserted-by":"publisher","unstructured":"Dwivedi, V. P. & Bresson, X. A generalization of transformer networks to graphs. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2012.09699 (2020).","DOI":"10.48550\/arXiv.2012.09699"},{"key":"849_CR60","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley, A. P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 30, 1145\u20131159 (1997).","journal-title":"Pattern Recognit."},{"key":"849_CR61","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.ins.2022.06.036","volume":"608","author":"Y Xue","year":"2022","unstructured":"Xue, Y., Tong, Y. & Neri, F. An ensemble of differential evolution and Adam for training feed-forward neural networks. Inf. Sci. 608, 453\u2013471 (2022).","journal-title":"Inf. Sci."},{"key":"849_CR62","doi-asserted-by":"publisher","unstructured":"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\u20137249 (NeurIPS, 2022); https:\/\/doi.org\/10.1101\/2022.06.06.495043","DOI":"10.1101\/2022.06.06.495043"},{"key":"849_CR63","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930\u2013D940 (2019).","journal-title":"Nucleic Acids Res."},{"key":"849_CR64","doi-asserted-by":"crossref","first-page":"D198","DOI":"10.1093\/nar\/gkl999","volume":"35","author":"T Liu","year":"2007","unstructured":"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\u2013D201 (2007).","journal-title":"Nucleic Acids Res."},{"key":"849_CR65","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1021\/ci049714+","volume":"45","author":"JJ Irwin","year":"2005","unstructured":"Irwin, J. J. & Shoichet, B. K. ZINC\u2014a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177\u2013182 (2005).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR66","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1021\/ci600426e","volume":"47","author":"JF Truchon","year":"2007","unstructured":"Truchon, J. F. & Bayly, C. I. Evaluating virtual screening methods: good and bad metrics for the \u2018early recognition\u2019 problem. J. Chem. Inf. Model. 47, 488\u2013508 (2007).","journal-title":"J. Chem. Inf. Model."},{"key":"849_CR67","doi-asserted-by":"publisher","unstructured":"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).","DOI":"10.5281\/zenodo.8049380"},{"key":"849_CR68","doi-asserted-by":"publisher","unstructured":"Cao, D., Chen, G., Jiang, J., Yu, J. & Zheng, M. TEST dataset pocket for EquiScore. Zenodo https:\/\/doi.org\/10.5281\/zenodo.8047224 (2023).","DOI":"10.5281\/zenodo.8047224"},{"key":"849_CR69","doi-asserted-by":"publisher","unstructured":"Cao, D. & Chen, G. Original data and supplementary information for \u2018EquiScore is a generic protein\u2013ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling\u2019. Zenodo https:\/\/doi.org\/10.5281\/zenodo.10812637 (2023).","DOI":"10.5281\/zenodo.10812637"},{"key":"849_CR70","doi-asserted-by":"crossref","unstructured":"Cao, D. Code for \u2018EquiScore is a generic protein\u2013ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling\u2019. GitHub https:\/\/github.com\/CAODH\/EquiScore (2023).","DOI":"10.1101\/2023.06.18.545464"},{"key":"849_CR71","doi-asserted-by":"publisher","unstructured":"Cao, D. Code for \u2018EquiScore is a generic protein\u2013ligand interaction scoring method integrating physical prior knowledge with data-augmentation modeling\u2019. Zenodo https:\/\/doi.org\/10.5281\/zenodo.10812534 (2023).","DOI":"10.5281\/zenodo.10812534"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00849-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00849-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00849-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:32:44Z","timestamp":1719606764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-024-00849-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,6]]},"references-count":71,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["849"],"URL":"https:\/\/doi.org\/10.1038\/s42256-024-00849-z","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,6]]},"assertion":[{"value":"22 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}