{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:04:44Z","timestamp":1773097484071,"version":"3.50.1"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T00:00:00Z","timestamp":1712448000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T00:00:00Z","timestamp":1712448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Foundation ARC pour la Recherche sur le Cancer"},{"DOI":"10.13039\/501100001320","name":"Wolfson Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001320","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000288","name":"Royal Society","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein\u2013ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which\u00a0achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1%\u2009=\u20090.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.<\/jats:p>","DOI":"10.1186\/s13321-024-00832-1","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T20:02:04Z","timestamp":1712433724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors"],"prefix":"10.1186","volume":"16","author":[{"given":"Klaudia","family":"Caba","sequence":"first","affiliation":[]},{"given":"Viet-Khoa","family":"Tran-Nguyen","sequence":"additional","affiliation":[]},{"given":"Taufiq","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Pedro J.","family":"Ballester","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,7]]},"reference":[{"key":"832_CR1","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1016\/j.molcel.2022.02.021","volume":"82","author":"D Huang","year":"2022","unstructured":"Huang D, Kraus WL (2022) The expanding universe of PARP1-mediated molecular and therapeutic mechanisms. Mol Cell 82:2315\u20132334. https:\/\/doi.org\/10.1016\/j.molcel.2022.02.021","journal-title":"Mol Cell"},{"key":"832_CR2","doi-asserted-by":"publisher","first-page":"7399","DOI":"10.1111\/febs.16142","volume":"289","author":"B L\u00fcscher","year":"2022","unstructured":"L\u00fcscher B, Ahel I, Altmeyer M et al (2022) ADP-ribosyltransferases, an update on function and nomenclature. FEBS J 289:7399\u20137410. https:\/\/doi.org\/10.1111\/febs.16142","journal-title":"FEBS J"},{"key":"832_CR3","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6807-11-37","author":"PA Loeffler","year":"2011","unstructured":"Loeffler PA, Cuneo MJ, Mueller GA et al (2011) Structural studies of the PARP-1 BRCT domain. BMC Struct Biol. https:\/\/doi.org\/10.1186\/1472-6807-11-37","journal-title":"BMC Struct Biol"},{"key":"832_CR4","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1073\/pnas.87.8.2990","volume":"87","author":"G Gradwohl","year":"1990","unstructured":"Gradwohl G, Mwnissier De Murcia J, Molinete M et al (1990) The second zinc-finger domain of poly(ADP-ribose) polymerase determines specificity for single-stranded breaks in DNA. Proc Nati Acad Sci USA 87:2990\u20132994","journal-title":"Proc Nati Acad Sci USA"},{"key":"832_CR5","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1038\/nsmb.2335","volume":"19","author":"AAE Ali","year":"2012","unstructured":"Ali AAE, Timinszky G, Arribas-Bosacoma R et al (2012) The zinc-finger domains of PARP1 cooperate to recognize DNA strand breaks. Nat Struct Mol Biol 19:685\u2013692. https:\/\/doi.org\/10.1038\/nsmb.2335","journal-title":"Nat Struct Mol Biol"},{"key":"832_CR6","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nrm3376","volume":"13","author":"BA Gibson","year":"2012","unstructured":"Gibson BA, Kraus WL (2012) New insights into the molecular and cellular functions of poly(ADP-ribose) and PARPs. Nat Rev Mol Cell Biol 13:411\u2013424","journal-title":"Nat Rev Mol Cell Biol"},{"key":"832_CR7","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1038\/nature08467","volume":"461","author":"SP Jackson","year":"2009","unstructured":"Jackson SP, Bartek J (2009) The DNA-damage response in human biology and disease. Nature 461:1071\u20131078","journal-title":"Nature"},{"key":"832_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ctrv.2018.12.002","volume":"73","author":"E Franzese","year":"2019","unstructured":"Franzese E, Centonze S, Diana A et al (2019) PARP inhibitors in ovarian cancer. Cancer Treat Rev 73:1\u20139","journal-title":"Cancer Treat Rev"},{"key":"832_CR9","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1056\/nejmoa1105535","volume":"366","author":"J Ledermann","year":"2012","unstructured":"Ledermann J, Harter P, Gourley C et al (2012) Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer. N Engl J Med 366:1382\u20131392. https:\/\/doi.org\/10.1056\/nejmoa1105535","journal-title":"N Engl J Med"},{"key":"832_CR10","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1093\/annonc\/mdz192","volume":"30","author":"J Mateo","year":"2019","unstructured":"Mateo J, Lord CJ, Serra V et al (2019) A decade of clinical development of PARP inhibitors in perspective. Ann Oncol 30:1437\u20131447. https:\/\/doi.org\/10.1093\/annonc\/mdz192","journal-title":"Ann Oncol"},{"key":"832_CR11","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1038\/s41573-020-0076-6","volume":"19","author":"NJ Curtin","year":"2020","unstructured":"Curtin NJ, Szabo C (2020) Poly(ADP-ribose) polymerase inhibition: past, present and future. Nat Rev Drug Discov 19:711\u2013736","journal-title":"Nat Rev Drug Discov"},{"key":"832_CR12","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1042\/bj1850775","volume":"185","author":"MR Purnell","year":"1980","unstructured":"Purnell MR, Whish WJD (1980) Novel Inhibitors of Poly(ADP-Ribose) synthetase. Biochem J 185:775\u2013777","journal-title":"Biochem J"},{"key":"832_CR13","doi-asserted-by":"publisher","first-page":"367","DOI":"10.2307\/3577927","volume":"126","author":"CM Arundel-Suto","year":"1991","unstructured":"Arundel-Suto CM, Scavone SV, Turner WR et al (1991) Effects of PD 128763, a new potent inhibitor of poly(ADP-ribose) polymerase, on X-ray-induced cellular recovery processes in Chinese hamster V79 cells. Radiat Res 126:367\u2013371","journal-title":"Radiat Res"},{"key":"832_CR14","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1016\/S0021-9258(18)45983-2","volume":"267","author":"M Banasik","year":"1992","unstructured":"Banasik M, Komura H, Shimoyama M, Ueda K (1992) Specific inhibitors of poly(ADP-Ribose) synthetase and mono(ADP-Ribosyl)transferase*. J Biol Chem 267:1569\u20131575","journal-title":"J Biol Chem"},{"key":"832_CR15","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nrd1718","volume":"4","author":"P Jagtap","year":"2005","unstructured":"Jagtap P, Szabo C (2005) Poly(ADP-ribose) polymerase and the therapeutic effects of its inhibitors. Nat Rev Drug Discov 4:421\u2013440","journal-title":"Nat Rev Drug Discov"},{"key":"832_CR16","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/nature03445","volume":"434","author":"H Farmer","year":"2005","unstructured":"Farmer H, McCabe H, Lord CJ et al (2005) Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434:917\u2013921. https:\/\/doi.org\/10.1038\/nature03445","journal-title":"Nature"},{"key":"832_CR17","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1038\/nature03443","volume":"434","author":"HE Bryant","year":"2005","unstructured":"Bryant HE, Schultz N, Thomas HD et al (2005) Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434:913\u2013917","journal-title":"Nature"},{"key":"832_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59074-4","author":"AA Antolin","year":"2020","unstructured":"Antolin AA, Ameratunga M, Banerji U et al (2020) The kinase polypharmacology landscape of clinical PARP inhibitors. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-020-59074-4","journal-title":"Sci Rep"},{"key":"832_CR19","doi-asserted-by":"publisher","first-page":"14498","DOI":"10.1021\/acs.jmedchem.1c01012","volume":"64","author":"JW Johannes","year":"2021","unstructured":"Johannes JW, Balazs A, Barratt D et al (2021) Discovery of 5-{4-[(7-Ethyl-6-oxo-5,6-dihydro-1,5-naphthyridin-3-yl)methyl]piperazin-1-yl}- N-methylpyridine-2-carboxamide (AZD5305): a PARP1-DNA trapper with high selectivity for PARP1 over PARP2 and other PARPs. J Med Chem 64:14498\u201314512. https:\/\/doi.org\/10.1021\/acs.jmedchem.1c01012","journal-title":"J Med Chem"},{"key":"832_CR20","doi-asserted-by":"publisher","first-page":"e15","DOI":"10.1016\/S1470-2045(18)30786-1","volume":"20","author":"CJ LaFargue","year":"2019","unstructured":"LaFargue CJ, Dal Molin GZ, Sood AK, Coleman RL (2019) Exploring and comparing adverse events between PARP inhibitors. Lancet Oncol 20:e15\u2013e28","journal-title":"Lancet Oncol"},{"key":"832_CR21","doi-asserted-by":"crossref","unstructured":"Gala UH, Miller DA, Williams RO (2020) Harnessing the therapeutic potential of anticancer drugs through amorphous solid dispersions. Biochim Biophys Acta Rev Cancer 1873","DOI":"10.1016\/j.bbcan.2019.188319"},{"key":"832_CR22","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.ejmech.2019.01.024","volume":"165","author":"PG Jain","year":"2019","unstructured":"Jain PG, Patel BD (2019) Medicinal chemistry approaches of poly ADP-Ribose polymerase 1 (PARP1) inhibitors as anticancer agents\u2014a recent update. Eur J Med Chem 165:198\u2013215","journal-title":"Eur J Med Chem"},{"key":"832_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1478","author":"H Li","year":"2021","unstructured":"Li H, Sze KH, Lu G, Ballester PJ (2021) Machine-learning scoring functions for structure-based virtual screening. Wiley Interdiscip Rev Comput Mol Sci. https:\/\/doi.org\/10.1002\/wcms.1478","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"832_CR24","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1038\/nrd1549","volume":"3","author":"DB Kitchen","year":"2004","unstructured":"Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935\u2013949","journal-title":"Nat Rev Drug Discov"},{"key":"832_CR25","doi-asserted-by":"publisher","first-page":"5912","DOI":"10.1021\/jm050362n","volume":"49","author":"GL Warren","year":"2006","unstructured":"Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912\u20135931. https:\/\/doi.org\/10.1021\/jm050362n","journal-title":"J Med Chem"},{"key":"832_CR26","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/jcc.21334","volume":"1","author":"O Trott","year":"2010","unstructured":"Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 1:455\u2013461. https:\/\/doi.org\/10.1002\/jcc.21334","journal-title":"J Comput Chem"},{"key":"832_CR27","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/jcc.21256","volume":"30","author":"GM Morris","year":"2009","unstructured":"Morris GM, Ruth H, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785\u20132791. https:\/\/doi.org\/10.1002\/jcc.21256","journal-title":"J Comput Chem"},{"key":"832_CR28","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","volume":"26","author":"PJ Ballester","year":"2010","unstructured":"Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169\u20131175. https:\/\/doi.org\/10.1093\/bioinformatics\/btq112","journal-title":"Bioinformatics"},{"key":"832_CR29","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1002\/wcms.1225","volume":"5","author":"QU Ain","year":"2015","unstructured":"Ain QU, Aleksandrova A, Roessler FD, Ballester PJ (2015) Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip Rev Comput Mol Sci 5:405\u2013424","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"832_CR30","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ejmech.2014.08.060","volume":"88","author":"B Hoeger","year":"2014","unstructured":"Hoeger B, Diether M, Ballester PJ, K\u00f6hn M (2014) Biochemical evaluation of virtual screening methods reveals a cell-active inhibitor of the cancer-promoting phosphatases of regenerating liver. Eur J Med Chem 88:89\u2013100. https:\/\/doi.org\/10.1016\/j.ejmech.2014.08.060","journal-title":"Eur J Med Chem"},{"key":"832_CR31","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10822-014-9732-4","volume":"28","author":"SP Patil","year":"2014","unstructured":"Patil SP, Ballester PJ, Kerezsi CR (2014) Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition. J Comput Aided Mol Des 28:89\u201397. https:\/\/doi.org\/10.1007\/s10822-014-9732-4","journal-title":"J Comput Aided Mol Des"},{"key":"832_CR32","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1021\/acs.jcim.5b00241","volume":"55","author":"JD Durrant","year":"2015","unstructured":"Durrant JD, Carlson KE, Martin TA et al (2015) Neural-network scoring functions identify structurally novel estrogen\u2013receptor ligands. J Chem Inf Model 55:1953\u20131961. https:\/\/doi.org\/10.1021\/acs.jcim.5b00241","journal-title":"J Chem Inf Model"},{"key":"832_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/srep24817","author":"H Sun","year":"2016","unstructured":"Sun H, Pan P, Tian S et al (2016) Constructing and validating high-performance MIEC-SVM models in virtual screening for kinases: a better way for actives discovery. Sci Rep. https:\/\/doi.org\/10.1038\/srep24817","journal-title":"Sci Rep"},{"key":"832_CR34","doi-asserted-by":"publisher","first-page":"8867","DOI":"10.1021\/acs.jmedchem.0c00473","volume":"63","author":"A Stecula","year":"2020","unstructured":"Stecula A, Hussain MS, Viola RE (2020) Discovery of novel inhibitors of a critical brain enzyme using a homology model and a deep convolutional neural network. J Med Chem 63:8867\u20138875. https:\/\/doi.org\/10.1021\/acs.jmedchem.0c00473","journal-title":"J Med Chem"},{"key":"832_CR35","doi-asserted-by":"publisher","first-page":"18477","DOI":"10.1073\/pnas.2000585117\/-\/DCSupplemental","volume":"117","author":"YO Adeshina","year":"2020","unstructured":"Adeshina YO, Deeds EJ, Karanicolas J (2020) Machine learning classification can reduce false positives in structure-based virtual screening. Proc Natl Acad Sci 117:18477\u201318488. https:\/\/doi.org\/10.1073\/pnas.2000585117\/-\/DCSupplemental","journal-title":"Proc Natl Acad Sci"},{"key":"832_CR36","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1098\/rsif.2012.0569","volume":"9","author":"PJ Ballester","year":"2012","unstructured":"Ballester PJ, Mangold M, Howard NI et al (2012) Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface 9:3196\u20133207. https:\/\/doi.org\/10.1098\/rsif.2012.0569","journal-title":"J R Soc Interface"},{"key":"832_CR37","doi-asserted-by":"publisher","first-page":"3989","DOI":"10.1093\/bioinformatics\/btz183","volume":"35","author":"H Li","year":"2019","unstructured":"Li H, Peng J, Sidorov P et al (2019) Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics 35:3989\u20133995. https:\/\/doi.org\/10.1093\/bioinformatics\/btz183","journal-title":"Bioinformatics"},{"key":"832_CR38","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa095","author":"L Fresnais","year":"2021","unstructured":"Fresnais L, Ballester PJ (2021) The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbaa095","journal-title":"Brief Bioinform"},{"key":"832_CR39","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1038\/s41596-023-00885-w","volume":"18","author":"V-K Tran-Nguyen","year":"2023","unstructured":"Tran-Nguyen V-K, Junaid M, Simeon S, Ballester PJ (2023) A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 18:3460\u20133511","journal-title":"Nat Protoc"},{"key":"832_CR40","doi-asserted-by":"publisher","first-page":"3374","DOI":"10.1038\/s41598-020-60221-0","volume":"10","author":"AC De Sousa","year":"2020","unstructured":"De Sousa AC, Combrinck JM, Maepa K et al (2020) Virtual screening as a tool to discover new \u03b2-haematin inhibitors with activity against malaria parasites. Sci Rep 10:3374","journal-title":"Sci Rep"},{"key":"832_CR41","doi-asserted-by":"publisher","first-page":"1288363","DOI":"10.3389\/fphar.2023.1288363","volume":"14","author":"R Dai","year":"2023","unstructured":"Dai R, Gao H, Su R (2023) Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2. Front Pharmacol 14:1288363. https:\/\/doi.org\/10.3389\/fphar.2023.1288363","journal-title":"Front Pharmacol"},{"key":"832_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/fddsv.2022.954911","volume":"2","author":"LA Machado","year":"2022","unstructured":"Machado LA, Krempser E, Guimar\u00e3es ACR (2022) A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Front Drug Discov 2:954911. https:\/\/doi.org\/10.3389\/fddsv.2022.954911","journal-title":"Front Drug Discov"},{"key":"832_CR43","unstructured":"PubChem, Poly [ADP-ribose] polymerase 1 (human), https:\/\/pubchem.ncbi.nlm.nih.gov\/protein\/P09874 (accessed on February 26, 2024)"},{"key":"832_CR44","doi-asserted-by":"publisher","first-page":"7830","DOI":"10.2174\/0929867328666210419134708","volume":"28","author":"S Simeon","year":"2021","unstructured":"Simeon S, Ghislat G, Ballester P (2021) Characterizing the relationship between the chemical structures of drugs and their activities on primary cultures of pediatric solid tumors. Curr Med Chem 28:7830\u20137839. https:\/\/doi.org\/10.2174\/0929867328666210419134708","journal-title":"Curr Med Chem"},{"key":"832_CR45","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.cbpa.2021.04.009","volume":"65","author":"G Ghislat","year":"2021","unstructured":"Ghislat G, Rahman T, Ballester PJ (2021) Recent progress on the prospective application of machine learning to structure-based virtual screening. Curr Opin Chem Biol 65:28\u201334","journal-title":"Curr Opin Chem Biol"},{"key":"832_CR46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"832_CR47","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. p 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"832_CR48","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support\u2014vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"832_CR49","first-page":"31","volume":"29","author":"AK Jain","year":"1996","unstructured":"Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial computer (Long Beach Calif) 29:31\u201344","journal-title":"Artificial neural networks: a tutorial computer (Long Beach Calif)"},{"key":"832_CR50","unstructured":"Abadi M, et al (2016) TensorFlow: a System for Large-Scale Machine Learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16). p 265\u2013283"},{"key":"832_CR51","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1093\/bioinformatics\/bty757","volume":"35","author":"M W\u00f3jcikowski","year":"2019","unstructured":"W\u00f3jcikowski M, Kukie\u0142ka M, Stepniewska-Dziubinska MM, Siedlecki P (2019) Development of a protein\u2013ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 35:1334\u20131341. https:\/\/doi.org\/10.1093\/bioinformatics\/bty757","journal-title":"Bioinformatics"},{"issue":"18193","key":"832_CR52","first-page":"18202","volume":"57","author":"S Zhong","year":"2023","unstructured":"Zhong S, Guan X (2023) Count-based morgan fingerprint: a more efficient and interpretable molecular representation in developing machine learning-based predictive regression models for water contaminants\u2019 activities and properties. Environ Sci Technol 57(18193):18202","journal-title":"Environ Sci Technol"},{"key":"832_CR53","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1021\/ci300604z","volume":"53","author":"DR Koes","year":"2013","unstructured":"Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53:1893\u20131904. https:\/\/doi.org\/10.1021\/ci300604z","journal-title":"J Chem Inf Model"},{"key":"832_CR54","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.jare.2022.07.001","volume":"46","author":"M McGibbon","year":"2023","unstructured":"McGibbon M, Money-Kyrle S, Blay V, Houston DR (2023) SCORCH: improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation. J Adv Res 46:135\u2013147. https:\/\/doi.org\/10.1016\/j.jare.2022.07.001","journal-title":"J Adv Res"},{"key":"832_CR55","doi-asserted-by":"publisher","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 DR (2017) Protein\u2013ligand scoring with convolutional neural networks. J Chem Inf Model 57:942\u2013957","journal-title":"J Chem Inf Model"},{"key":"832_CR56","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1021\/ci600426e","volume":"47","author":"JF Truchon","year":"2007","unstructured":"Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the \u201cearly recognition\u201d problem. J Chem Inf Model 47:488\u2013508. https:\/\/doi.org\/10.1021\/ci600426e","journal-title":"J Chem Inf Model"},{"key":"832_CR57","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1021\/acs.jcim.8b00363","volume":"59","author":"S Liu","year":"2019","unstructured":"Liu S, Alnammi M, Ericksen SS et al (2019) Practical Model Selection for Prospective Virtual Screening. J Chem Inf Model 59:282\u2013293","journal-title":"J Chem Inf Model"},{"key":"832_CR58","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1186\/1471-2105-15-291","volume":"15","author":"H Li","year":"2014","unstructured":"Li H, Leung K-S, Wong M-H, Ballester PJ (2014) Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinform 15:291","journal-title":"BMC Bioinform"},{"key":"832_CR59","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00522-2","author":"AT McNutt","year":"2021","unstructured":"McNutt AT, Francoeur P, Aggarwal R et al (2021) GNINA 1.0: molecular docking with deep learning. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-021-00522-2","journal-title":"J Cheminform"},{"key":"832_CR60","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. https:\/\/doi.org\/10.1021\/jm300687e","journal-title":"J Med Chem"},{"key":"832_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/molecules26237369","author":"J Sunseri","year":"2021","unstructured":"Sunseri J, Koes DR (2021) Virtual screening with gnina 1.0. Molecules. https:\/\/doi.org\/10.3390\/molecules26237369","journal-title":"Molecules"},{"key":"832_CR62","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa410","author":"C Shen","year":"2021","unstructured":"Shen C, Weng G, Zhang X et al (2021) Accuracy or novelty: What can we gain from target-specific machine-learning-based scoring functions in virtual screening? Brief. https:\/\/doi.org\/10.1093\/bib\/bbaa410","journal-title":"Brief"},{"key":"832_CR63","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa070","author":"C Shen","year":"2021","unstructured":"Shen C, Hu Y, Wang Z et al (2021) Beware of the generic machine learning-based scoring functions in structure-based virtual screening. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbaa070","journal-title":"Brief Bioinform"},{"key":"832_CR64","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1465","author":"H Li","year":"2020","unstructured":"Li H, Sze KH, Lu G, Ballester PJ (2020) Machine-learning scoring functions for structure-based drug lead optimization. Wiley Interdiscip Rev Comput Mol Sci. https:\/\/doi.org\/10.1002\/wcms.1465","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"832_CR65","doi-asserted-by":"publisher","DOI":"10.1038\/srep46710","author":"M W\u00f3jcikowski","year":"2017","unstructured":"W\u00f3jcikowski M, Ballester PJ, Siedlecki P (2017) Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep. https:\/\/doi.org\/10.1038\/srep46710","journal-title":"Sci Rep"},{"key":"832_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2024.01.024","author":"P G\u00f3mez-Sacrist\u00e1n","year":"2024","unstructured":"G\u00f3mez-Sacrist\u00e1n P, Simeon S, Tran-Nguyen VK et al (2024) Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers. J Adv Res. https:\/\/doi.org\/10.1016\/j.jare.2024.01.024","journal-title":"J Adv Res"},{"key":"832_CR67","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742\u2013754. https:\/\/doi.org\/10.1021\/ci100050t","journal-title":"J Chem Inf Model"},{"key":"832_CR68","doi-asserted-by":"publisher","first-page":"8373","DOI":"10.1039\/d0cp00305k","volume":"22","author":"K Gao","year":"2020","unstructured":"Gao K, Nguyen DD, Sresht V et al (2020) Are 2D fingerprints still valuable for drug discovery? Phys Chem Chem Phys 22:8373\u20138390. https:\/\/doi.org\/10.1039\/d0cp00305k","journal-title":"Phys Chem Chem Phys"},{"key":"832_CR69","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jcim.3c00218","volume":"63","author":"VK Tran-Nguyen","year":"2023","unstructured":"Tran-Nguyen VK, Ballester PJ (2023) Beware of simple methods for structure-based virtual screening: the critical importance of broader comparisons. J Chem Inf Model 63:1401\u20131405. https:\/\/doi.org\/10.1021\/acs.jcim.3c00218","journal-title":"J Chem Inf Model"},{"key":"832_CR70","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1093\/bioinformatics\/btz665","volume":"36","author":"F Boyles","year":"2020","unstructured":"Boyles F, Deane CM, Morris GM (2020) Learning from the ligand: using ligand-based features to improve binding affinity prediction. Bioinformatics 36:758\u2013764","journal-title":"Bioinformatics"},{"key":"832_CR71","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13321-021-00516-0","volume":"13","author":"M Thomas","year":"2021","unstructured":"Thomas M, Smith RT, O\u2019Boyle NM et al (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13:39","journal-title":"J Cheminform"},{"key":"832_CR72","doi-asserted-by":"publisher","first-page":"8494","DOI":"10.1080\/07391102.2021.1913229","volume":"40","author":"M Singh","year":"2022","unstructured":"Singh M, Rajawat J, Kuldeep J et al (2022) Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor. J Biomol Struct Dyn 40:8494\u20138507. https:\/\/doi.org\/10.1080\/07391102.2021.1913229","journal-title":"J Biomol Struct Dyn"},{"key":"832_CR73","doi-asserted-by":"publisher","DOI":"10.3390\/molecules24234258","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Tang S, Chen T, Niu MM (2019) Structure-based pharmacophore modeling, virtual screening, molecular docking and biological evaluation for identification of potential poly (ADP-Ribose) polymerase-1 (PARP-1) inhibitors. Molecules. https:\/\/doi.org\/10.3390\/molecules24234258","journal-title":"Molecules"},{"key":"832_CR74","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0122-0","author":"D Chen","year":"2019","unstructured":"Chen D, Liu S, Kingsbury P et al (2019) Deep learning and alternative learning strategies for retrospective real-world clinical data. NPJ Digit Med. https:\/\/doi.org\/10.1038\/s41746-019-0122-0","journal-title":"NPJ Digit Med"},{"key":"832_CR75","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01041","author":"A Bomane","year":"2019","unstructured":"Bomane A, Gon\u00e7alves A, Ballester PJ (2019) Paclitaxel response can be predicted with interpretable multi-variate classifiers exploiting DNA-methylation and miRNA Data. Front Genet. https:\/\/doi.org\/10.3389\/fgene.2019.01041","journal-title":"Front Genet"},{"key":"832_CR76","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3229161","author":"V Borisov","year":"2022","unstructured":"Borisov V, Leemann T, Se\u00dfler K et al (2022) Deep neural networks and tabular data: a survey. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3229161","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"832_CR77","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ddtec.2020.09.001","volume":"32\u201333","author":"PJ Ballester","year":"2019","unstructured":"Ballester PJ (2019) Selecting machine-learning scoring functions for structure-based virtual screening. Drug Discov Today Technol 32\u201333:81\u201387","journal-title":"Drug Discov Today Technol"},{"key":"832_CR78","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1021\/ci500091r","volume":"54","author":"PJ Ballester","year":"2014","unstructured":"Ballester PJ, Schreyer A, Blundell TL (2014) Does a more precise chemical description of protein\u2013ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model 54:944\u2013955","journal-title":"J Chem Inf Model"},{"key":"832_CR79","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/d41586-023-03948-w","volume":"624","author":"PJ Ballester","year":"2023","unstructured":"Ballester PJ (2023) The AI revolution in chemistry is not that far away. Nature 624:252","journal-title":"Nature"},{"key":"832_CR80","doi-asserted-by":"publisher","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","volume":"45","author":"A Gaulton","year":"2017","unstructured":"Gaulton A, Hersey A, Nowotka ML et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45:D945\u2013D954. https:\/\/doi.org\/10.1093\/nar\/gkw1074","journal-title":"Nucleic Acids Res"},{"key":"832_CR81","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.chemolab.2015.12.002","volume":"151","author":"S Simeon","year":"2015","unstructured":"Simeon S, M\u00f6ller R, Almgren D et al (2015) Unraveling the origin of splice switching activity of hemoglobin \u03b2-globin gene modulators via QSAR modeling. Chemom Intell Lab Syst 151:51\u201360","journal-title":"Chemom Intell Lab Syst"},{"key":"832_CR82","doi-asserted-by":"publisher","DOI":"10.1074\/JBC.RA120.016573","author":"K Ryan","year":"2021","unstructured":"Ryan K, Bola\u00f1os B, Smith M et al (2021) Dissecting the molecular determinants of clinical PARP1 inhibitor selectivity for tankyrase. J Biol Chem. https:\/\/doi.org\/10.1074\/JBC.RA120.016573","journal-title":"J Biol Chem"},{"key":"832_CR83","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235\u2013242","journal-title":"Nucleic Acids Res"},{"key":"832_CR84","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33","author":"NM O\u2019Boyle","year":"2011","unstructured":"O\u2019Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical\u2014toolbox. J Cheminform. https:\/\/doi.org\/10.1186\/1758-2946-3-33","journal-title":"J Cheminform"},{"key":"832_CR85","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1002\/jcc.20084","volume":"25","author":"EF Pettersen","year":"2004","unstructured":"Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera - A visualization system for exploratory research and analysis. J Comput Chem 25:1605\u20131612. https:\/\/doi.org\/10.1002\/jcc.20084","journal-title":"J Comput Chem"},{"key":"832_CR86","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1002\/jcc.10128","volume":"23","author":"A Jakalian","year":"2002","unstructured":"Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II Parameterization and validation. J Comput Chem 23:1623\u20131641. https:\/\/doi.org\/10.1002\/jcc.10128","journal-title":"J Comput Chem"},{"key":"832_CR87","doi-asserted-by":"publisher","first-page":"4574","DOI":"10.3390\/ijms20184574","volume":"20","author":"PHM Torres","year":"2019","unstructured":"Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP (2019) Key topics in molecular docking for drug design. Int J Mol Sci 20:4574","journal-title":"Int J Mol Sci"},{"key":"832_CR88","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1021\/acscentsci.8b00507","volume":"4","author":"EN Feinberg","year":"2018","unstructured":"Feinberg EN, Sur D, Wu Z et al (2018) PotentialNet for molecular property prediction. ACS Cent Sci 4:1520\u20131530. https:\/\/doi.org\/10.1021\/acscentsci.8b00507","journal-title":"ACS Cent Sci"},{"key":"832_CR89","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-015-0078-2","author":"M W\u00f3jcikowski","year":"2015","unstructured":"W\u00f3jcikowski M, Zielenkiewicz P, Siedlecki P (2015) Open Drug Discovery Toolkit (ODDT): A new open-source player in the drug discovery field. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-015-0078-2","journal-title":"J Cheminform"},{"key":"832_CR90","unstructured":"Chollet F (2015) Keras. In: https:\/\/github.com\/fchollet\/keras. https:\/\/keras.io. Accessed 15 Nov 2023"},{"key":"832_CR91","doi-asserted-by":"publisher","first-page":"14008","DOI":"10.1088\/1749-4699\/8\/1\/014008","volume":"8","author":"J Bergstra","year":"2015","unstructured":"Bergstra J, Komer B, Eliasmith C et al (2015) Hyperopt: a Python library for model selection and hyperparameter optimization. Comput Sci Discov 8:14008","journal-title":"Comput Sci Discov"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00832-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-024-00832-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00832-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T20:02:32Z","timestamp":1712433752000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-024-00832-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,7]]},"references-count":91,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["832"],"URL":"https:\/\/doi.org\/10.1186\/s13321-024-00832-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.03.15.585277","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,7]]},"assertion":[{"value":"16 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 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":"All other authors declare they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40"}}