{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:43:20Z","timestamp":1775119400552,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020M3A9G7103933"],"award-info":[{"award-number":["NRF-2020M3A9G7103933"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003654","name":"Korea Environmental Industry and Technology Institute","doi-asserted-by":"publisher","award":["RS-2023-00219144"],"award-info":[{"award-number":["RS-2023-00219144"]}],"id":[{"id":"10.13039\/501100003654","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003654","name":"Korea Environmental Industry and Technology Institute","doi-asserted-by":"publisher","award":["RS-2023-00219144"],"award-info":[{"award-number":["RS-2023-00219144"]}],"id":[{"id":"10.13039\/501100003654","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We introduce an advanced model for predicting protein\u2013ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein\u2013ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein\u2013ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein\u2013ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model\u2019s efficiency and generalizability. The model\u2019s efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Scientific contribution<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>Our work introduces a novel training strategy for a protein\u2013ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.<\/jats:p>","DOI":"10.1186\/s13321-024-00912-2","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T11:03:14Z","timestamp":1730718194000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Accurate prediction of protein\u2013ligand interactions by combining physical energy functions and graph-neural networks"],"prefix":"10.1186","volume":"16","author":[{"given":"Yiyu","family":"Hong","sequence":"first","affiliation":[]},{"given":"Junsu","family":"Ha","sequence":"additional","affiliation":[]},{"given":"Jaemin","family":"Sim","sequence":"additional","affiliation":[]},{"given":"Chae Jo","family":"Lim","sequence":"additional","affiliation":[]},{"given":"Kwang-Seok","family":"Oh","sequence":"additional","affiliation":[]},{"given":"Ramakrishnan","family":"Chandrasekaran","sequence":"additional","affiliation":[]},{"given":"Bomin","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Jieun","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Junsu","family":"Ko","sequence":"additional","affiliation":[]},{"given":"Woong-Hee","family":"Shin","sequence":"additional","affiliation":[]},{"given":"Juyong","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"912_CR1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ddtec.2021.08.001","volume":"39","author":"L Frye","year":"2021","unstructured":"Frye L, Bhat S, Akinsanya K, Abel R (2021) From computer-aided drug discovery to computer-driven drug discovery. Drug Discov Today Technol 39:111\u2013117","journal-title":"Drug Discov Today Technol"},{"key":"912_CR2","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1021\/acs.jcim.7b00564","volume":"57","author":"Z Cournia","year":"2017","unstructured":"Cournia Z, Allen B, Sherman W (2017) Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf Model 57:2911\u20132937","journal-title":"J Chem Inf Model"},{"key":"912_CR3","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1517\/17460441.2015.1032936","volume":"10","author":"S Genheden","year":"2015","unstructured":"Genheden S, Ryde U (2015) The MM\/PBSA and MM\/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449\u2013461","journal-title":"Expert Opin Drug Discov"},{"key":"912_CR4","doi-asserted-by":"publisher","first-page":"6358","DOI":"10.1073\/pnas.1303186110","volume":"110","author":"V Limongelli","year":"2013","unstructured":"Limongelli V, Bonomi M, Parrinello M (2013) Funnel metadynamics as accurate binding free-energy method. Proc Natl Acad Sci 110:6358\u20136363","journal-title":"Proc Natl Acad Sci"},{"key":"912_CR5","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1016\/j.jmb.2007.06.002","volume":"371","author":"DL Mobley","year":"2007","unstructured":"Mobley DL et al (2007) Predicting absolute ligand binding free energies to a simple model site. J Mol Biol 371:1118\u20131134","journal-title":"J Mol Biol"},{"key":"912_CR6","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1021\/acs.jctc.1c01288","volume":"18","author":"AP Bhati","year":"2022","unstructured":"Bhati AP, Coveney PV (2022) Large scale study of ligand-protein relative binding free energy calculations: actionable predictions from statistically robust protocols. J Chem Theory Comput 18:2687\u20132702","journal-title":"J Chem Theory Comput"},{"key":"912_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3390\/life12010054","volume":"12","author":"J Byun","year":"2021","unstructured":"Byun J, Lee J (2021) Identifying the hot spot residues of the SARS-CoV-2 main protease using MM-PBSA and multiple force fields. Life 12:54","journal-title":"Life"},{"key":"912_CR8","doi-asserted-by":"publisher","first-page":"4799","DOI":"10.1038\/s41596-021-00597-z","volume":"16","author":"BJ Bender","year":"2021","unstructured":"Bender BJ et al (2021) A practical guide to large-scale docking. Nat Protoc 16:4799\u20134832","journal-title":"Nat Protoc"},{"key":"912_CR9","doi-asserted-by":"publisher","first-page":"7317","DOI":"10.3390\/molecules27217317","volume":"27","author":"L Guo","year":"2022","unstructured":"Guo L et al (2022) Ultra-large-scale screening of natural compounds and free energy calculations revealed potential inhibitors for the receptor-binding domain (RBD) of SARS-CoV-2. Molecules 27:7317","journal-title":"Molecules"},{"key":"912_CR10","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/jcc.21256","volume":"30","author":"GM Morris","year":"2009","unstructured":"Morris GM et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785\u20132791","journal-title":"J Comput Chem"},{"key":"912_CR11","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1002\/jcc.23905","volume":"36","author":"WJ Allen","year":"2015","unstructured":"Allen WJ et al (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36:1132\u20131156","journal-title":"J Comput Chem"},{"key":"912_CR12","doi-asserted-by":"crossref","unstructured":"Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model 61:3891\u20133898","DOI":"10.1021\/acs.jcim.1c00203"},{"key":"912_CR13","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","volume":"59","author":"M Su","year":"2019","unstructured":"Su M et al (2019) Comparative assessment of scoring functions: the CASF-2016 update. J Chem Inf Model 59:895\u2013913","journal-title":"J Chem Inf Model"},{"key":"912_CR14","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1023\/A:1016357811882","volume":"16","author":"R Wang","year":"2002","unstructured":"Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11\u201326","journal-title":"J Comput Aided Mol Des"},{"key":"912_CR15","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/1074-5521(95)90050-0","volume":"2","author":"DK Gehlhaar","year":"1995","unstructured":"Gehlhaar DK et al (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2:317\u2013324","journal-title":"Chem Biol"},{"key":"912_CR16","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1021\/ci800298z","volume":"49","author":"O Korb","year":"2009","unstructured":"Korb O, St\u00fctzle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49:84\u201396","journal-title":"J Chem Inf Model"},{"key":"912_CR17","doi-asserted-by":"crossref","unstructured":"Friesner RA et al. (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739\u20131749","DOI":"10.1021\/jm0306430"},{"key":"912_CR18","doi-asserted-by":"crossref","unstructured":"Li H, Sze K, Lu G, Ballester PJ (2021) Machine\u2010learning scoring functions for structure\u2010based virtual screening. WIREs Comput Mol Sci 11(1):e1478","DOI":"10.1002\/wcms.1478"},{"key":"912_CR19","doi-asserted-by":"crossref","unstructured":"Shen C et al. (2020) From machine learning to deep learning: advances in scoring functions for protein\u2013ligand docking. WIREs Comput Mol Sci 10(1):e1429","DOI":"10.1002\/wcms.1429"},{"key":"912_CR20","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1006\/jmbi.1999.3371","volume":"295","author":"H Gohlke","year":"2000","unstructured":"Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337\u2013356","journal-title":"J Mol Biol"},{"key":"912_CR21","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1021\/jm980536j","volume":"42","author":"I Muegge","year":"1999","unstructured":"Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42:791\u2013804","journal-title":"J Med Chem"},{"key":"912_CR22","doi-asserted-by":"publisher","first-page":"12964","DOI":"10.1039\/C6CP01555G","volume":"18","author":"Z Wang","year":"2016","unstructured":"Wang Z et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys PCCP 18:12964\u201312975","journal-title":"Phys Chem Chem Phys PCCP"},{"key":"912_CR23","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1021\/acs.jcim.7b00650","volume":"58","author":"J Jim\u00e9nez","year":"2018","unstructured":"Jim\u00e9nez J, \u0160kali\u010d M, Mart\u00ednez-Rosell G, De Fabritiis G (2018) KDEEP: protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J Chem Inf Model 58:287\u2013296","journal-title":"J Chem Inf Model"},{"key":"912_CR24","doi-asserted-by":"publisher","first-page":"15956","DOI":"10.1021\/acsomega.9b01997","volume":"4","author":"L Zheng","year":"2019","unstructured":"Zheng L, Fan J, Mu Y (2019) OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega 4:15956\u201315965","journal-title":"ACS Omega"},{"key":"912_CR25","doi-asserted-by":"publisher","first-page":"3666","DOI":"10.1093\/bioinformatics\/bty374","volume":"34","author":"MM Stepniewska-Dziubinska","year":"2018","unstructured":"Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P (2018) Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinforma Oxf Engl 34:3666\u20133674","journal-title":"Bioinforma Oxf Engl"},{"key":"912_CR26","doi-asserted-by":"publisher","first-page":"8424","DOI":"10.3390\/ijms21228424","volume":"21","author":"Y Kwon","year":"2020","unstructured":"Kwon Y, Shin W-H, Ko J, Lee J (2020) AK-score: accurate protein-ligand binding affinity prediction using an ensemble of 3D-convolutional neural networks. Int J Mol Sci 21:8424","journal-title":"Int J Mol Sci"},{"key":"912_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2022.885983","volume":"2","author":"R Meli","year":"2022","unstructured":"Meli R, Morris GM, Biggin PC (2022) Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review. Front Bioinforma 2:885983","journal-title":"Front Bioinforma"},{"key":"912_CR28","doi-asserted-by":"publisher","unstructured":"Zhang X et al. (2023) PLANET: a multi-objective graph neural network model for protein\u2013ligand binding affinity prediction. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.3c00253.","DOI":"10.1021\/acs.jcim.3c00253"},{"key":"912_CR29","doi-asserted-by":"crossref","unstructured":"Wang K, Zhou R, Tang J, Li M (2023) GraphscoreDTA: optimized graph neural network for protein\u2013ligand binding affinity prediction. Bioinformatics 39: btad340","DOI":"10.1093\/bioinformatics\/btad340"},{"key":"912_CR30","doi-asserted-by":"crossref","unstructured":"Yang Z, Zhong W, Lv Q, Dong T, Yu-Chian C (2023) Geometric interaction graph neural network for predicting protein\u2013ligand binding affinities from 3D structures (GIGN). J Phys Chem Lett 14:2020\u20132033","DOI":"10.1021\/acs.jpclett.2c03906"},{"key":"912_CR31","doi-asserted-by":"publisher","first-page":"18209","DOI":"10.1021\/acs.jmedchem.1c01830","volume":"64","author":"D Jiang","year":"2021","unstructured":"Jiang D et al (2021) InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions. J Med Chem 64:18209\u201318232","journal-title":"J Med Chem"},{"key":"912_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0249404","volume":"16","author":"J Son","year":"2021","unstructured":"Son J, Kim D (2021) Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PLoS ONE 16:e0249404","journal-title":"PLoS ONE"},{"key":"912_CR33","doi-asserted-by":"publisher","first-page":"22496","DOI":"10.1021\/acsomega.3c00085","volume":"8","author":"S Zhang","year":"2023","unstructured":"Zhang S et al (2023) SS-GNN: a simple-structured graph neural network for affinity prediction. ACS Omega 8:22496\u201322507","journal-title":"ACS Omega"},{"key":"912_CR34","doi-asserted-by":"crossref","unstructured":"Shen C et al. (2021) Beware of the generic machine learning-based scoring functions in structure-based virtual screening. Brief Bioinform 22:bbaa070","DOI":"10.1093\/bib\/bbaa070"},{"key":"912_CR35","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1039\/D1SC06946B","volume":"13","author":"S Moon","year":"2022","unstructured":"Moon S, Zhung W, Yang S, Lim J, Kim WY (2022) PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chem Sci 13:3661\u20133673","journal-title":"Chem Sci"},{"key":"912_CR36","doi-asserted-by":"publisher","first-page":"10691","DOI":"10.1021\/acs.jmedchem.2c00991","volume":"65","author":"C Shen","year":"2022","unstructured":"Shen C et al (2022) 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","journal-title":"J Med Chem"},{"key":"912_CR37","doi-asserted-by":"publisher","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 EA, Wegner JK (2021) A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat Mach Intell 3:1033\u20131039","journal-title":"Nat Mach Intell"},{"key":"912_CR38","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1021\/jm300687e","volume":"55","author":"MM Mysinger","year":"2012","unstructured":"Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582\u20136594","journal-title":"J Med Chem"},{"key":"912_CR39","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","volume":"60","author":"V-K Tran-Nguyen","year":"2020","unstructured":"Tran-Nguyen V-K, Jacquemard C, Rognan D (2020) LIT-PCBA: an unbiased data set for machine learning and virtual screening. J Chem Inf Model 60:4263\u20134273","journal-title":"J Chem Inf Model"},{"key":"912_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2023.117374","volume":"90","author":"C Zhang","year":"2023","unstructured":"Zhang C et al (2023) Recent research advances in ATX inhibitors: an overview of primary literature. Bioorg Med Chem 90:117374","journal-title":"Bioorg Med Chem"},{"key":"912_CR41","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s13321-021-00501-7","volume":"13","author":"Y Kwon","year":"2021","unstructured":"Kwon Y, Lee J (2021) MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES. J Cheminformatics 13:24","journal-title":"J Cheminformatics"},{"key":"912_CR42","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1021\/acs.accounts.6b00491","volume":"50","author":"Z Liu","year":"2017","unstructured":"Liu Z et al (2017) Forging the basis for developing protein-ligand interaction scoring functions. Acc Chem Res 50:302\u2013309","journal-title":"Acc Chem Res"},{"key":"912_CR43","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1021\/acs.jctc.0c01006","volume":"17","author":"D Santos-Martins","year":"2021","unstructured":"Santos-Martins D et al (2021) Accelerating AutoDock4 with GPUs and gradient-based local search. J Chem Theory Comput 17:1060\u20131073","journal-title":"J Chem Theory Comput"},{"key":"912_CR44","doi-asserted-by":"publisher","unstructured":"Landrum G et al. (2021) rdkit\/rdkit: 2021_09_1 (Q3 2021) Release. Zenodo https:\/\/doi.org\/10.5281\/ZENODO.5578915","DOI":"10.5281\/ZENODO.5578915"},{"key":"912_CR45","doi-asserted-by":"crossref","unstructured":"Grygorenko OO (2021) Enamine Ltd.: the science and business of organic chemistry and beyond. Eur J Org Chem 2021:6474\u20136477.","DOI":"10.1002\/ejoc.202101210"},{"key":"912_CR46","doi-asserted-by":"crossref","unstructured":"Schuffenhauer A et al. (2005) Library design for fragment based screening. Curr Top Med Chem 5(8):751\u2013762","DOI":"10.2174\/1568026054637700"},{"key":"912_CR47","doi-asserted-by":"crossref","unstructured":"Jacoby E et al. Key Aspects of the novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection. Curr Top Med Chem 5(4):397\u2013411","DOI":"10.2174\/1568026053828376"},{"key":"912_CR48","doi-asserted-by":"crossref","unstructured":"Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking\/scoring combinations. J Med Chem 43:4759\u20134767","DOI":"10.1021\/jm001044l"},{"key":"912_CR49","unstructured":"Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks? Preprint at http:\/\/arxiv.org\/abs\/2105.14491"},{"key":"912_CR50","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch geometric. Preprint at http:\/\/arxiv.org\/abs\/1903.02428 (2019)."},{"key":"912_CR51","unstructured":"Agarap AF (2019) Deep learning using rectified linear units (ReLU). Preprint at http:\/\/arxiv.org\/abs\/1803.08375"},{"key":"912_CR52","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"912_CR53","unstructured":"Paszke A et al. (2019) PyTorch: an imperative style, high-performance deep learning library. Preprint at http:\/\/arxiv.org\/abs\/1912.01703"},{"key":"912_CR54","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. Preprint at http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"912_CR55","doi-asserted-by":"crossref","unstructured":"Zheng L et al. (2022) Improving protein\u2013ligand docking and screening accuracies by incorporating a scoring function correction term. Brief Bioinform 23:bbac051","DOI":"10.1093\/bib\/bbac051"},{"key":"912_CR56","doi-asserted-by":"publisher","first-page":"11635","DOI":"10.3390\/ijms222111635","volume":"22","author":"J Choi","year":"2021","unstructured":"Choi J, Lee J (2021) V-Dock: fast generation of novel drug-like molecules using machine-learning-based docking score and molecular optimization. Int J Mol Sci 22:11635","journal-title":"Int J Mol Sci"},{"key":"912_CR57","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90\u201398","journal-title":"Nat Chem"},{"key":"912_CR58","doi-asserted-by":"publisher","first-page":"8129","DOI":"10.1039\/D3SC02044D","volume":"14","author":"C Shen","year":"2023","unstructured":"Shen C et al (2023) A generalized protein\u2013ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem Sci 14:8129\u20138146","journal-title":"Chem Sci"},{"key":"912_CR59","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.1021\/jm048957q","volume":"48","author":"R Wang","year":"2005","unstructured":"Wang R, Fang X, Lu Y, Yang C-Y, Wang S (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111\u20134119","journal-title":"J Med Chem"},{"key":"912_CR60","doi-asserted-by":"crossref","unstructured":"Wang Z et al. (2024) A new paradigm for applying deep learning to protein\u2013ligand interaction prediction. Brief Bioinform 25:bbae145","DOI":"10.1093\/bib\/bbae145"},{"key":"912_CR61","doi-asserted-by":"crossref","unstructured":"Wang Z et al. (2023) A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. Brief Bioinform 24:bbac520","DOI":"10.1093\/bib\/bbac520"},{"key":"912_CR62","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1039\/D3DD00149K","volume":"3","author":"S Moon","year":"2024","unstructured":"Moon S, Hwang S-Y, Lim J, Kim WY (2024) PIGNet2: a versatile deep learning-based protein\u2013ligand interaction prediction model for binding affinity scoring and virtual screening. Digit Discov 3:287\u2013299","journal-title":"Digit Discov"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00912-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-024-00912-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00912-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T12:05:38Z","timestamp":1730721938000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-024-00912-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["912"],"URL":"https:\/\/doi.org\/10.1186\/s13321-024-00912-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3887850\/v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"22 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors hereby provide consent for the publication of the manuscript detailed above, including any accompanying images or data contained within the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"121"}}