{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T22:42:43Z","timestamp":1782340963968,"version":"3.54.5"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/MolecularAI\/DockStream\">https:\/\/github.com\/MolecularAI\/DockStream<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00563-7","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T06:03:18Z","timestamp":1637128998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["DockStream: a docking wrapper to enhance de novo molecular design"],"prefix":"10.1186","volume":"13","author":[{"given":"Jeff","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon Paul","family":"Janet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias R.","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Nittinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn A.","family":"Giblin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kostas","family":"Papadopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexey","family":"Voronov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atanas","family":"Patronov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Engkvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5473-6318","authenticated-orcid":false,"given":"Christian","family":"Margreitter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"issue":"5","key":"563_CR1","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","volume":"19","author":"P Schneider","year":"2020","unstructured":"Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G (2020) Rethinking Drug design in the artificial intelligence era. Nat Rev Drug Discov 19(5):353\u2013364. https:\/\/doi.org\/10.1038\/s41573-019-0050-3","journal-title":"Nat Rev Drug Discov"},{"key":"563_CR2","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2021.1909567","author":"J Jim\u00e9nez-Luna","year":"2021","unstructured":"Jim\u00e9nez-Luna J, Grisoni F, Weskamp N, Schneider G (2021) Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov. https:\/\/doi.org\/10.1080\/17460441.2021.1909567","journal-title":"Expert Opin Drug Discov"},{"issue":"8","key":"563_CR3","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","volume":"27","author":"PG Polishchuk","year":"2013","unstructured":"Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27(8):675\u2013679. https:\/\/doi.org\/10.1007\/s10822-013-9672-4","journal-title":"J Comput Aided Mol Des"},{"key":"563_CR4","doi-asserted-by":"publisher","unstructured":"REINVENT 2.0: An AI tool for de novo drug design. J Chem Inf Model. Doi: https:\/\/doi.org\/10.1021\/acs.jcim.0c00915. Accessed 14 Jun 2021.","DOI":"10.1021\/acs.jcim.0c00915"},{"issue":"1","key":"563_CR5","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13321-018-0286-7","volume":"10","author":"J Lim","year":"2018","unstructured":"Lim J, Ryu S, Kim JW, Kim WY (2018) Molecular generative model based on conditional variational autoencoder for de novo molecular design. J Cheminformatics 10(1):31. https:\/\/doi.org\/10.1186\/s13321-018-0286-7","journal-title":"J Cheminformatics"},{"issue":"1","key":"563_CR6","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13321-019-0404-1","volume":"12","author":"\u0141 Maziarka","year":"2020","unstructured":"Maziarka \u0141, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warcho\u0142 M (2020) Mol-CycleGAN: a generative model for molecular optimization. J Cheminformatics 12(1):2. https:\/\/doi.org\/10.1186\/s13321-019-0404-1","journal-title":"J Cheminformatics"},{"issue":"2","key":"563_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abcf91","volume":"2","author":"R Mercado","year":"2021","unstructured":"Mercado R, Rastemo T, Lindel\u00f6f E, Klambauer G, Engkvist O, Chen H, Jannik Bjerrum E (2021) Graph networks for molecular design. Mach Learn Sci Technol 2(2):025023. https:\/\/doi.org\/10.1088\/2632-2153\/abcf91","journal-title":"Mach Learn Sci Technol"},{"issue":"7","key":"563_CR8","doi-asserted-by":"publisher","first-page":"eaap7885","DOI":"10.1126\/sciadv.aap7885","volume":"4","author":"M Popova","year":"2018","unstructured":"Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de novo drug design. Sci Adv 4(7):eaap7885. https:\/\/doi.org\/10.1126\/sciadv.aap7885","journal-title":"Sci Adv"},{"issue":"1","key":"563_CR9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13321-021-00498-z","volume":"13","author":"T Pereira","year":"2021","unstructured":"Pereira T, Abbasi M, Ribeiro B, Arrais JP (2021) Diversity oriented deep reinforcement learning for targeted molecule generation. J Cheminformatics 13(1):21. https:\/\/doi.org\/10.1186\/s13321-021-00498-z","journal-title":"J Cheminformatics"},{"issue":"23","key":"563_CR10","doi-asserted-by":"publisher","first-page":"4559","DOI":"10.1042\/BCJ20200781","volume":"477","author":"DB Kell","year":"2020","unstructured":"Kell DB, Samanta S, Swainston N (2020) Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 477(23):4559\u20134580. https:\/\/doi.org\/10.1042\/BCJ20200781","journal-title":"Biochem J"},{"issue":"1","key":"563_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-017-0256-5","volume":"10","author":"S Kausar","year":"2018","unstructured":"Kausar S, Falcao AO (2018) An automated framework for QSAR model building. J Cheminformatics 10(1):1. https:\/\/doi.org\/10.1186\/s13321-017-0256-5","journal-title":"J Cheminformatics"},{"issue":"6","key":"563_CR12","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1021\/acsomega.7b00274","volume":"2","author":"L Zhao","year":"2017","unstructured":"Zhao L, Wang W, Sedykh A, Zhu H (2017) Experimental errors in QSAR modeling sets: what we can do and what we cannot do. ACS Omega 2(6):2805\u20132812. https:\/\/doi.org\/10.1021\/acsomega.7b00274","journal-title":"ACS Omega"},{"issue":"18","key":"563_CR13","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.3390\/ijms20184331","volume":"20","author":"L Pinzi","year":"2019","unstructured":"Pinzi L, Rastelli G (2019) Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci 20(18):4331. https:\/\/doi.org\/10.3390\/ijms20184331","journal-title":"Int J Mol Sci"},{"issue":"6","key":"563_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00894-019-4032-5","volume":"25","author":"AM El Kerdawy","year":"2019","unstructured":"El Kerdawy AM, Osman AA, Zaater MA (2019) Receptor-based pharmacophore modeling, virtual screening, and molecular docking studies for the discovery of novel GSK-3\u03b2 inhibitors. J Mol Model 25(6):171. https:\/\/doi.org\/10.1007\/s00894-019-4032-5","journal-title":"J Mol Model"},{"issue":"6","key":"563_CR15","doi-asserted-by":"publisher","first-page":"3265","DOI":"10.1021\/acs.jcim.0c00171","volume":"60","author":"W Zhao","year":"2020","unstructured":"Zhao W, Xiong M, Yuan X, Li M, Sun H, Xu Y (2020) In silico screening-based discovery of novel inhibitors of human cyclic GMP\u2013AMP synthase: a cross-validation study of molecular docking and experimental testing. J Chem Inf Model 60(6):3265\u20133276. https:\/\/doi.org\/10.1021\/acs.jcim.0c00171","journal-title":"J Chem Inf Model"},{"issue":"7743","key":"563_CR16","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","volume":"566","author":"J Lyu","year":"2019","unstructured":"Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O\u2019Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ (2019) Ultra-large library docking for discovering new chemotypes. Nature 566(7743):224\u2013229. https:\/\/doi.org\/10.1038\/s41586-019-0917-9","journal-title":"Nature"},{"issue":"3","key":"563_CR17","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.cbpa.2010.02.018","volume":"14","author":"ME Welsch","year":"2010","unstructured":"Welsch ME, Snyder SA, Stockwell BR (2010) Privileged scaffolds for library design and drug discovery. Curr Opin Chem Biol 14(3):347\u2013361. https:\/\/doi.org\/10.1016\/j.cbpa.2010.02.018","journal-title":"Curr Opin Chem Biol"},{"issue":"1","key":"563_CR18","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, de Graaf C, Bender A (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: A GPCR case study. J Cheminformatics 13(1):39. https:\/\/doi.org\/10.1186\/s13321-021-00516-0","journal-title":"J Cheminformatics"},{"key":"563_CR19","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv.14371967.v1","author":"B Ma","year":"2021","unstructured":"Ma B, Terayama K, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y (2021) Structure-based de novo molecular generator combined with artificial intelligence and docking simulations. J Chem Inf Model. https:\/\/doi.org\/10.26434\/chemrxiv.14371967.v1","journal-title":"J Chem Inf Model"},{"key":"563_CR20","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa161","author":"Q Bai","year":"2021","unstructured":"Bai Q, Tan S, Xu T, Liu H, Huang J, Yao X (2021) MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbaa161","journal-title":"Brief Bioinform"},{"issue":"1","key":"563_CR21","doi-asserted-by":"publisher","first-page":"22104","DOI":"10.1038\/s41598-020-78537-2","volume":"10","author":"W Jeon","year":"2020","unstructured":"Jeon W, Kim D (2020) Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Sci Rep 10(1):22104. https:\/\/doi.org\/10.1038\/s41598-020-78537-2","journal-title":"Sci Rep"},{"issue":"11","key":"563_CR22","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1002\/jcc.21498","volume":"31","author":"X Li","year":"2010","unstructured":"Li X, Li Y, Cheng T, Liu Z, Wang R (2010) Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J Comput Chem 31(11):2109\u20132125. https:\/\/doi.org\/10.1002\/jcc.21498","journal-title":"J Comput Chem"},{"issue":"18","key":"563_CR23","doi-asserted-by":"publisher","first-page":"12964","DOI":"10.1039\/C6CP01555G","volume":"18","author":"Z Wang","year":"2016","unstructured":"Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, Tian S, Hou T (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 18(18):12964\u201312975. https:\/\/doi.org\/10.1039\/C6CP01555G","journal-title":"Phys Chem Chem Phys"},{"key":"563_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/ijms17020144","author":"X Du","year":"2016","unstructured":"Du X, Li Y, Xia Y-L, Ai S-M, Liang J, Sang P, Ji X-L, Liu S-Q (2016) Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms17020144","journal-title":"Int J Mol Sci"},{"key":"563_CR25","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1146\/annurev-biophys-083012-130318","volume":"42","author":"JD Chodera","year":"2013","unstructured":"Chodera JD, Mobley DL (2013) Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu Rev Biophys 42:121\u2013142. https:\/\/doi.org\/10.1146\/annurev-biophys-083012-130318","journal-title":"Annu Rev Biophys"},{"issue":"6","key":"563_CR26","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1021\/ci400115b","volume":"53","author":"MR Bauer","year":"2013","unstructured":"Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM (2013) Evaluation and optimization of virtual screening workflows with DEKOIS 2.0\u2014a public library of challenging docking benchmark sets. J Chem Inf Model 53(6):1447\u20131462. https:\/\/doi.org\/10.1021\/ci400115b","journal-title":"J Chem Inf Model"},{"issue":"4","key":"563_CR27","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci00020a039","volume":"34","author":"J Sadowski","year":"1994","unstructured":"Sadowski J, Gasteiger J, Klebe G (1994) Comparison of automatic three-dimensional model builders using 639 X-Ray structures. J Chem Inf Comput Sci 34(4):1000\u20131008. https:\/\/doi.org\/10.1021\/ci00020a039","journal-title":"J Chem Inf Comput Sci"},{"issue":"4","key":"563_CR28","doi-asserted-by":"publisher","first-page":"e245","DOI":"10.1016\/j.ddtec.2010.10.003","volume":"7","author":"CH Schwab","year":"2010","unstructured":"Schwab CH (2010) Conformations and 3D pharmacophore searching. Drug Discov Today Technol 7(4):e245\u2013e253. https:\/\/doi.org\/10.1016\/j.ddtec.2010.10.003","journal-title":"Drug Discov Today Technol"},{"key":"563_CR29","unstructured":"3D Structure Generator CORINA Classic, Molecular Networks GmbH, Nuremberg, Germany, www.Mn-Am.Com."},{"key":"563_CR30","unstructured":"Schr\u00f6dinger Release 2019-4: LigPrep, Schr\u00f6dinger, LLC, New York, NY, 2019."},{"issue":"4","key":"563_CR31","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1021\/ci100031x","volume":"50","author":"PCD Hawkins","year":"2010","unstructured":"Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50(4):572\u2013584. https:\/\/doi.org\/10.1021\/ci100031x","journal-title":"J Chem Inf Model"},{"key":"563_CR32","unstructured":"OMEGA 4.1.0.2: OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com."},{"issue":"2","key":"563_CR33","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/jcc.21334","volume":"31","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 31(2):455\u2013461. https:\/\/doi.org\/10.1002\/jcc.21334","journal-title":"J Comput Chem"},{"issue":"7","key":"563_CR34","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1021\/jm0306430","volume":"47","author":"RA Friesner","year":"2004","unstructured":"Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739\u20131749. https:\/\/doi.org\/10.1021\/jm0306430","journal-title":"J Med Chem"},{"issue":"7","key":"563_CR35","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1021\/jm030644s","volume":"47","author":"TA Halgren","year":"2004","unstructured":"Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750\u20131759. https:\/\/doi.org\/10.1021\/jm030644s","journal-title":"J Med Chem"},{"issue":"21","key":"563_CR36","doi-asserted-by":"publisher","first-page":"6177","DOI":"10.1021\/jm051256o","volume":"49","author":"RA Friesner","year":"2006","unstructured":"Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177\u20136196. https:\/\/doi.org\/10.1021\/jm051256o","journal-title":"J Med Chem"},{"key":"563_CR37","unstructured":"Schr\u00f6dinger Release 2019-4: Glide, Schr\u00f6dinger, LLC, New York, NY, 2019."},{"issue":"3","key":"563_CR38","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1006\/jmbi.1996.0897","volume":"267","author":"G Jones","year":"1997","unstructured":"Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking11edited by F. E. Cohen. J Mol Biol 267(3):727\u2013748. https:\/\/doi.org\/10.1006\/jmbi.1996.0897","journal-title":"J Mol Biol"},{"key":"563_CR39","unstructured":"OEDOCKING 4.0.0.2: OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com."},{"issue":"8","key":"563_CR40","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s10822-012-9584-8","volume":"26","author":"M McGann","year":"2012","unstructured":"McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26(8):897\u2013906. https:\/\/doi.org\/10.1007\/s10822-012-9584-8","journal-title":"J Comput Aided Mol Des"},{"issue":"4","key":"563_CR41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003571","volume":"10","author":"S Ruiz-Carmona","year":"2014","unstructured":"Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P, Barril X, Hubbard RE, Morley SD (2014) RDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLOS Comput Biol 10(4):e1003571. https:\/\/doi.org\/10.1371\/journal.pcbi.1003571","journal-title":"PLOS Comput Biol"},{"key":"563_CR42","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-1-60327-216-2_23","volume":"823","author":"AC Anderson","year":"2012","unstructured":"Anderson AC (2012) Structure-based functional design of drugs: from target to lead compound. Methods Mol Biol Clifton NJ 823:359\u2013366. https:\/\/doi.org\/10.1007\/978-1-60327-216-2_23","journal-title":"Methods Mol Biol Clifton NJ"},{"issue":"8","key":"563_CR43","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1021\/acs.jcim.5b00142","volume":"55","author":"BP Kelley","year":"2015","unstructured":"Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 55(8):1771\u20131780. https:\/\/doi.org\/10.1021\/acs.jcim.5b00142","journal-title":"J Chem Inf Model"},{"issue":"3","key":"563_CR44","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10822-008-9181-z","volume":"22","author":"RD Clark","year":"2008","unstructured":"Clark RD, Webster-Clark DJ (2008) Managing bias in ROC curves. J Comput Aided Mol Des 22(3):141\u2013146. https:\/\/doi.org\/10.1007\/s10822-008-9181-z","journal-title":"J Comput Aided Mol Des"},{"key":"563_CR45","doi-asserted-by":"crossref","unstructured":"Zwillinger, D. and Kokoska, S. (2000). CRC standard probability and statistics tables and formulae. Chapman & Hall: New York; 2000. Section 14.7.","DOI":"10.1201\/9780367802417"},{"issue":"3","key":"563_CR46","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/biomet\/33.3.239","volume":"33","author":"MG Kendall","year":"1945","unstructured":"Kendall MG (1945) The treatment of ties in ranking problems. Biometrika 33(3):239\u2013251. https:\/\/doi.org\/10.1093\/biomet\/33.3.239","journal-title":"Biometrika"},{"issue":"1","key":"563_CR47","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s13321-020-00473-0","volume":"12","author":"T Blaschke","year":"2020","unstructured":"Blaschke T, Engkvist O, Bajorath J, Chen H (2020) Memory-assisted reinforcement learning for diverse molecular de novo design. J Cheminformatics 12(1):68. https:\/\/doi.org\/10.1186\/s13321-020-00473-0","journal-title":"J Cheminformatics"},{"issue":"1","key":"563_CR48","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13321-019-0393-0","volume":"11","author":"J Ar\u00fas-Pous","year":"2019","unstructured":"Ar\u00fas-Pous J, Johansson SV, Prykhodko O, Bjerrum EJ, Tyrchan C, Reymond J-L, Chen H, Engkvist O (2019) Randomized SMILES strings improve the quality of molecular generative models. J Cheminformatics 11(1):71. https:\/\/doi.org\/10.1186\/s13321-019-0393-0","journal-title":"J Cheminformatics"},{"key":"563_CR49","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. ArXiv14061078 Cs Stat 2014.","DOI":"10.3115\/v1\/D14-1179"},{"issue":"D1","key":"563_CR50","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 M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibri\u00e1n-Uhalte E, Davies M, Dedman N, Karlsson A, Magari\u00f1os MP, Overington JP, Papadatos G, Smit I, Leach AR (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945\u2013D954. https:\/\/doi.org\/10.1093\/nar\/gkw1074","journal-title":"Nucleic Acids Res"},{"key":"563_CR51","doi-asserted-by":"publisher","unstructured":"Korshunova M, Huang N, Capuzzi S, Radchenko DS, Savych O, Moroz YS, Wells C, Willson TM, Tropsha A, Isayev O. A bag of tricks for automated de novo design of molecules with the desired properties: application to EGFR inhibitor discovery. 2021. Doi: https:\/\/doi.org\/10.26434\/chemrxiv.14045072.v1.","DOI":"10.26434\/chemrxiv.14045072.v1"},{"issue":"10","key":"563_CR52","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1517\/17460441.2012.714363","volume":"7","author":"JA Arnott","year":"2012","unstructured":"Arnott JA, Planey SL (2012) The influence of lipophilicity in drug discovery and design. Expert Opin Drug Discov 7(10):863\u2013875. https:\/\/doi.org\/10.1517\/17460441.2012.714363","journal-title":"Expert Opin Drug Discov"},{"issue":"7","key":"563_CR53","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","volume":"53","author":"JB Baell","year":"2010","unstructured":"Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53(7):2719\u20132740. https:\/\/doi.org\/10.1021\/jm901137j","journal-title":"J Med Chem"},{"key":"563_CR54","doi-asserted-by":"publisher","unstructured":"Kenny PW, Sadowski J. Structure modification in chemical databases. In: Chemoinformatics in drug discovery. John Wiley & Sons, Ltd; 2005. p. 271\u2013285. Doi: https:\/\/doi.org\/10.1002\/3527603743.ch11.","DOI":"10.1002\/3527603743.ch11"},{"key":"563_CR55","unstructured":"Daylight Theory: SMIRKS\u2014a reaction transform language https:\/\/www.daylight.com\/dayhtml\/doc\/theory\/theory.smirks.html. Accessed 14 Jun 2021."},{"issue":"1","key":"563_CR56","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","volume":"23","author":"CA Lipinski","year":"1997","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1):3\u201325. https:\/\/doi.org\/10.1016\/S0169-409X(96)00423-1","journal-title":"Adv Drug Deliv Rev"},{"issue":"2","key":"563_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(2):90\u201398. https:\/\/doi.org\/10.1038\/nchem.1243","journal-title":"Nat Chem"},{"key":"563_CR58","unstructured":"Wu Y, Choma N, Chen A, Cashman M, Prates \u00c9T, Shah M, Vergara VGM, Clyde A, Brettin TS, de Jong WA, Kumar N, Head MS, Stevens RL, Nugent P, Jacobson DA, Brown JB. Spatial graph attention and curiosity-driven policy for antiviral drug discovery. ArXiv210602190 Cs Q-Bio. 2021."},{"key":"563_CR59","unstructured":"Rolnick D, Ahuja A, Schwarz J, Lillicrap TP, Wayne G. Experience replay for continual learning. ArXiv181111682 Cs Stat. 2019."},{"issue":"6610","key":"563_CR60","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1038\/384644a0","volume":"384","author":"RG Kurumbail","year":"1996","unstructured":"Kurumbail RG, Stevens AM, Gierse JK, McDonald JJ, Stegeman RA, Pak JY, Gildehaus D, Iyashiro JM, Penning TD, Seibert K, Isakson PC, Stallings WC (1996) Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature 384(6610):644\u2013648. https:\/\/doi.org\/10.1038\/384644a0","journal-title":"Nature"},{"issue":"6","key":"563_CR61","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5772\/intechopen.68318","volume":"23","author":"PK Deb","year":"2017","unstructured":"Deb PK, Mailabaram RP, Al-Jaidi B, Saadh M (2017) Molecular basis of binding interactions of NSAIDs and computer-aided drug design approaches in the pursuit of the development of cyclooxygenase-2 (COX-2) selective inhibitors. Nonsteroidal Anti Inflamm Drugs. 23(6):101\u2013121. https:\/\/doi.org\/10.5772\/intechopen.68318","journal-title":"Nonsteroidal Anti Inflamm Drugs."},{"issue":"9","key":"563_CR62","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1080\/03610910903168603","volume":"38","author":"L Ferreira","year":"2009","unstructured":"Ferreira L, Hitchcock DB (2009) A comparison of hierarchical methods for clustering functional data. Commun Stat Simul Comput 38(9):1925\u20131949. https:\/\/doi.org\/10.1080\/03610910903168603","journal-title":"Commun Stat Simul Comput"},{"issue":"1","key":"563_CR63","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.bmcl.2009.10.102","volume":"20","author":"MA Argiriadi","year":"2010","unstructured":"Argiriadi MA, Ericsson AM, Harris CM, Banach DL, Borhani DW, Calderwood DJ, Demers MD, DiMauro J, Dixon RW, Hardman J, Kwak S, Li B, Mankovich JA, Marcotte D, Mullen KD, Ni B, Pietras M, Sadhukhan R, Sousa S, Tomlinson MJ, Wang L, Xiang T, Talanian RV (2010) 2,4-diaminopyrimidine MK2 inhibitors. Part I: observation of an unexpected inhibitor binding mode. Bioorg Med Chem Lett 20(1):330\u2013333. https:\/\/doi.org\/10.1016\/j.bmcl.2009.10.102","journal-title":"Bioorg Med Chem Lett"},{"issue":"10","key":"563_CR64","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1016\/j.bmcl.2010.03.091","volume":"20","author":"JC Bressi","year":"2010","unstructured":"Bressi JC, Jennings AJ, Skene R, Wu Y, Melkus R, Jong RD, O\u2019Connell S, Grimshaw CE, Navre M, Gangloff AR (2010) Exploration of the HDAC2 foot pocket: synthesis and SAR of substituted N-(2-aminophenyl)benzamides. Bioorg Med Chem Lett 20(10):3142\u20133145. https:\/\/doi.org\/10.1016\/j.bmcl.2010.03.091","journal-title":"Bioorg Med Chem Lett"},{"issue":"44","key":"563_CR65","doi-asserted-by":"publisher","first-page":"17323","DOI":"10.1073\/pnas.0705356104","volume":"104","author":"V Nahoum","year":"2007","unstructured":"Nahoum V, Perez E, Germain P, Rodriguez-Barrios F, Manzo F, Kammerer S, Lemaire G, Hirsch O, Royer CA, Gronemeyer H, de Lera AR, Bourguet W (2007) Modulators of the structural dynamics of the retinoid X receptor to reveal receptor function. Proc Natl Acad Sci 104(44):17323\u201317328. https:\/\/doi.org\/10.1073\/pnas.0705356104","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"563_CR66","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1158\/0008-5472.CAN-12-2038","volume":"73","author":"G-H Wang","year":"2013","unstructured":"Wang G-H, Jiang F-Q, Duan Y-H, Zeng Z-P, Chen F, Dai Y, Chen J-B, Liu J-X, Liu J, Zhou H, Chen H-F, Zeng J-Z, Su Y, Yao X-S, Zhang X-K (2013) Targeting truncated retinoid X receptor-\u03b1 by CF31 induces TNF-\u03b1\u2013dependent apoptosis. Cancer Res 73(1):307\u2013318. https:\/\/doi.org\/10.1158\/0008-5472.CAN-12-2038","journal-title":"Cancer Res"},{"issue":"5","key":"563_CR67","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1110\/ps.47601","volume":"10","author":"R Almog","year":"2001","unstructured":"Almog R, Waddling CA, Maley F, Maley GF, Roey PV (2001) Crystal structure of a deletion mutant of human thymidylate synthase \u0394 (7\u201329) and its ternary complex with tomudex and DUMP. Protein Sci 10(5):988\u2013996. https:\/\/doi.org\/10.1110\/ps.47601","journal-title":"Protein Sci"},{"key":"563_CR68","unstructured":"McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. ArXiv180203426 Cs Stat 2020."},{"key":"563_CR69","doi-asserted-by":"publisher","unstructured":"Rogers D, Hahn M. Extended-connectivity fingerprints. https:\/\/doi.org\/10.1021\/ci100050t. Accessed 14 Jun 2021.","DOI":"10.1021\/ci100050t"},{"issue":"1","key":"563_CR70","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/1741-7007-9-71","volume":"9","author":"JD Durrant","year":"2011","unstructured":"Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9(1):71. https:\/\/doi.org\/10.1186\/1741-7007-9-71","journal-title":"BMC Biol"},{"issue":"8","key":"563_CR71","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1021\/acs.jcim.9b00383","volume":"59","author":"ST Kurkinen","year":"2019","unstructured":"Kurkinen ST, L\u00e4tti S, Pentik\u00e4inen OT, Postila PA (2019) Getting docking into shape using negative image-based rescoring. J Chem Inf Model 59(8):3584\u20133599. https:\/\/doi.org\/10.1021\/acs.jcim.9b00383","journal-title":"J Chem Inf Model"},{"key":"563_CR72","doi-asserted-by":"publisher","DOI":"10.1101\/2021.04.06.438722","author":"S Gu","year":"2021","unstructured":"Gu S, Smith MS, Yang Y, Irwin JJ, Shoichet BK (2021) Ligand Strain energy in large library docking. bioRxiv. https:\/\/doi.org\/10.1101\/2021.04.06.438722","journal-title":"bioRxiv"},{"key":"563_CR73","doi-asserted-by":"publisher","unstructured":"Eberhardt J, Santos-Martins D, Tillack A, Forli S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. 2021. https:\/\/doi.org\/10.26434\/chemrxiv.14774223.v1.","DOI":"10.26434\/chemrxiv.14774223.v1"},{"key":"563_CR74","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2017.0070","author":"N Kudo","year":"2018","unstructured":"Kudo N, Ito A, Arata M, Nakata A, Yoshida M (2018) Identification of a novel small molecule that inhibits deacetylase but not defatty-acylase reaction catalysed by SIRT2. Philos Trans R Soc B Biol Sci. https:\/\/doi.org\/10.1098\/rstb.2017.0070","journal-title":"Philos Trans R Soc B Biol Sci."},{"issue":"3","key":"563_CR75","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s10822-013-9644-8","volume":"27","author":"G Madhavi Sastry","year":"2013","unstructured":"Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221\u2013234. https:\/\/doi.org\/10.1007\/s10822-013-9644-8","journal-title":"J Comput Aided Mol Des"},{"key":"563_CR76","unstructured":"Schr\u00f6dinger release 2019-4: protein preparation wizard; Epik, Schr\u00f6dinger, LLC, New York, NY, 2016; Impact, Schr\u00f6dinger, LLC, New York, NY, 2016; Prime, Schr\u00f6dinger, LLC, New York, NY, 2019."},{"issue":"3","key":"563_CR77","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1021\/acs.jctc.8b01026","volume":"15","author":"K Roos","year":"2019","unstructured":"Roos K, Wu C, Damm W, Reboul M, Stevenson JM, Lu C, Dahlgren MK, Mondal S, Chen W, Wang L, Abel R, Friesner RA, Harder ED (2019) OPLS3e: extending force field coverage for drug-like small molecules. J Chem Theory Comput 15(3):1863\u20131874. https:\/\/doi.org\/10.1021\/acs.jctc.8b01026","journal-title":"J Chem Theory Comput"},{"issue":"1","key":"563_CR78","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1021\/ct300857j","volume":"9","author":"P Eastman","year":"2013","unstructured":"Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang L-P, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS (2013) OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theory Comput 9(1):461\u2013469. https:\/\/doi.org\/10.1021\/ct300857j","journal-title":"J Chem Theory Comput"},{"issue":"1","key":"563_CR79","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/1758-2946-3-33","volume":"3","author":"NM O\u2019Boyle","year":"2011","unstructured":"O\u2019Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminformatics 3(1):33. https:\/\/doi.org\/10.1186\/1758-2946-3-33","journal-title":"J Cheminformatics"},{"key":"563_CR80","unstructured":"Schr\u00f6dinger Release 2019-4: Maestro, Schr\u00f6dinger, LLC, New York, NY, 2019."},{"issue":"2","key":"563_CR81","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1107\/S2052520616003954","volume":"72","author":"CR Groom","year":"2016","unstructured":"Groom CR, Bruno IJ, Lightfoot MP, Ward SC (2016) The Cambridge structural database. Acta Crystallogr Sect B Struct Sci Cryst Eng Mater. 72(2):171\u2013179. https:\/\/doi.org\/10.1107\/S2052520616003954","journal-title":"Acta Crystallogr Sect B Struct Sci Cryst Eng Mater."},{"key":"563_CR82","doi-asserted-by":"publisher","unstructured":"Rappe AK, Casewit CJ, Colwell KS, Goddard WA III, Skiff WM. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. https:\/\/doi.org\/10.1021\/ja00051a040. Accessed 14 Jun 2021.","DOI":"10.1021\/ja00051a040"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00563-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00563-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00563-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T06:15:54Z","timestamp":1637129754000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-021-00563-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":82,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["563"],"URL":"https:\/\/doi.org\/10.1186\/s13321-021-00563-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2021-qvhml","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]},"assertion":[{"value":"5 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}