{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T16:11:46Z","timestamp":1793722306638,"version":"4.6.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"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":"crossref","award":["2017R1E1A1A01076642"],"award-info":[{"award-number":["2017R1E1A1A01076642"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2020R1I1A1A01074750"],"award-info":[{"award-number":["2020R1I1A1A01074750"]}],"id":[{"id":"10.13039\/501100003725","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>\n                    In drug discovery, rapid and accurate prediction of protein\u2013ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein\u2013ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein\u2013Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson\u2019s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in p\n                    <jats:italic>K<\/jats:italic>\n                    units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00507-1","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T10:03:04Z","timestamp":1616666584000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors"],"prefix":"10.1186","volume":"13","author":[{"given":"Surendra","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-5637","authenticated-orcid":false,"given":"Mi-hyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"507_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1146\/annurev.biophys.36.040306.132550","volume":"36","author":"MK Gilson","year":"2007","unstructured":"Gilson MK, Zhou H-X (2007) Calculation of protein-ligand binding affinities. Ann Rev Biophys Biomol Structure 36:21\u201342","journal-title":"Ann Rev Biophys Biomol Structure"},{"issue":"20","key":"507_CR2","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.2174\/1568026617666170224121313","volume":"17","author":"D Bajusz","year":"2017","unstructured":"Bajusz D, Ferenczy GG, Keseru MG (2017) Structure-based virtual screening approaches in kinase-directed drug discovery. Curr Top Med Chem 17(20):2235\u20132259","journal-title":"Curr Top Med Chem"},{"issue":"3","key":"507_CR3","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(3):758\u2013764","journal-title":"Bioinformatics"},{"issue":"5","key":"507_CR4","doi-asserted-by":"publisher","first-page":"603","DOI":"10.4155\/fmc.12.18","volume":"4","author":"P Ripphausen","year":"2012","unstructured":"Ripphausen P, Stumpfe D, Bajorath J (2012) Analysis of structure-based virtual screening studies and characterization of identified active compounds. Future Med Chem 4(5):603\u2013613","journal-title":"Future Med Chem"},{"issue":"44","key":"507_CR5","doi-asserted-by":"publisher","first-page":"5166","DOI":"10.1039\/B608269F","volume":"8","author":"N Huang","year":"2006","unstructured":"Huang N, Kalyanaraman C, Bernacki K, Jacobson MP (2006) Molecular mechanics methods for predicting protein-ligand binding. Phys Chem Chem Phys 8(44):5166\u20135177","journal-title":"Phys Chem Chem Phys"},{"issue":"7","key":"507_CR6","doi-asserted-by":"publisher","first-page":"2695","DOI":"10.1021\/ja512751q","volume":"137","author":"L Wang","year":"2015","unstructured":"Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137(7):2695\u20132703","journal-title":"J Am Chem Soc"},{"issue":"5","key":"507_CR7","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(5):449\u2013461","journal-title":"Expert Opin Drug Discov"},{"issue":"1","key":"507_CR8","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1021\/ci100275a","volume":"51","author":"T Hou","year":"2011","unstructured":"Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM\/PBSA and MM\/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51(1):69\u201382","journal-title":"J Chem Inf Model"},{"issue":"16","key":"507_CR9","doi-asserted-by":"publisher","first-page":"4805","DOI":"10.1021\/jm060522a","volume":"49","author":"PD Lyne","year":"2006","unstructured":"Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 49(16):4805\u20134808","journal-title":"J Med Chem"},{"issue":"7","key":"507_CR10","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 (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","journal-title":"J Med Chem"},{"issue":"3","key":"507_CR11","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 docking. J Mol Biol 267(3):727\u2013748","journal-title":"J Mol Biol"},{"issue":"16","key":"507_CR12","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/jcc.21256","volume":"30","author":"GM Morris","year":"2009","unstructured":"Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785\u20132791","journal-title":"J Comput Chem"},{"issue":"2","key":"507_CR13","doi-asserted-by":"crossref","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","journal-title":"J Comput Chem"},{"issue":"3","key":"507_CR14","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1021\/ci300493w","volume":"53","author":"G-B Li","year":"2013","unstructured":"Li G-B, Yang L-L, Wang W-J, Li L-L, Yang S-Y (2013) ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions. J Chem Inf Model 53(3):592\u2013600","journal-title":"J Chem Inf Model"},{"issue":"3","key":"507_CR15","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1021\/ci500731a","volume":"55","author":"J Liu","year":"2015","unstructured":"Liu J, Wang R (2015) Classification of current scoring functions. J Chem Inf Model 55(3):475\u2013482","journal-title":"J Chem Inf Model"},{"issue":"4","key":"507_CR16","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1021\/jm020406h","volume":"46","author":"AN Jain","year":"2003","unstructured":"Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46(4):499\u2013511","journal-title":"J Med Chem"},{"issue":"8","key":"507_CR17","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1021\/ci400040d","volume":"53","author":"KM Elokely","year":"2013","unstructured":"Elokely KM, Doerksen RJ (2013) Docking challenge: protein sampling and molecular docking performance. J Chem Inf Model 53(8):1934\u20131945","journal-title":"J Chem Inf Model"},{"issue":"40","key":"507_CR18","doi-asserted-by":"publisher","first-page":"12899","DOI":"10.1039\/c0cp00151a","volume":"12","author":"S-Y Huang","year":"2010","unstructured":"Huang S-Y, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 12(40):12899\u201312908","journal-title":"Phys Chem Chem Phys"},{"issue":"1","key":"507_CR19","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/prot.21082","volume":"65","author":"SF Sousa","year":"2006","unstructured":"Sousa SF, Fernandes PA, Ramos MJ (2006) Protein\u2013ligand docking: current status and future challenges. Proteins Struct Funct Bioinform 65(1):15\u201326","journal-title":"Proteins Struct Funct Bioinform"},{"key":"507_CR20","doi-asserted-by":"crossref","unstructured":"Loh W-Y (2014) Classification and regression tree methods. Wiley StatsRef: Statistics Reference Online","DOI":"10.1002\/9781118445112.stat03886"},{"key":"507_CR21","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","volume":"42","author":"Q Zhang","year":"2018","unstructured":"Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for Big Data. Inf Fusion 42:146\u2013157","journal-title":"Inf Fusion"},{"issue":"6","key":"507_CR22","doi-asserted-by":"publisher","first-page":"129545","DOI":"10.1016\/j.bbagen.2020.129545","volume":"1864","author":"SR Ellingson","year":"2020","unstructured":"Ellingson SR, Davis B, Allen J (2020) Machine learning and ligand binding predictions: a review of data, methods, and obstacles. Biochim Biophys Acta Gen Subj 1864(6):129545","journal-title":"Biochim Biophys Acta Gen Subj"},{"key":"507_CR23","unstructured":"Gomes J, Ramsundar B, Feinberg EN, Pande VS (2017) Atomic convolutional networks for predicting protein-ligand binding affinity. arXiv preprint arXiv:1703.10603"},{"issue":"2","key":"507_CR24","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, Skalic M, Martinez-Rosell G, De Fabritiis G (2018) K Deep: protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J Chem Inf Model 58(2):287\u2013296","journal-title":"J Chem Inf Model"},{"key":"507_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Rezaei MA, Li C, Li X (2019) DeepAtom: a framework for protein-ligand binding affinity prediction, IEEE, pp 303\u2013310","DOI":"10.1109\/BIBM47256.2019.8982964"},{"key":"507_CR26","unstructured":"Wallach I, Dzamba M, Heifets A (2015) AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:1510.02855"},{"issue":"4","key":"507_CR27","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-ligand scoring with convolutional neural networks. J Chem Inf Model 57(4):942\u2013957","journal-title":"J Chem Inf Model"},{"issue":"21","key":"507_CR28","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. Bioinformatics 34(21):3666\u20133674","journal-title":"Bioinformatics"},{"issue":"6","key":"507_CR29","doi-asserted-by":"publisher","first-page":"2791","DOI":"10.1021\/acs.jcim.0c00075","volume":"60","author":"H Hassan-Harrirou","year":"2020","unstructured":"Hassan-Harrirou H, Zhang C, Lemmin T (2020) RosENet: improving binding affinity prediction by leveraging molecular mechanics energies with an ensemble of 3D convolutional neural networks. J Chem Inf Model 60(6):2791\u20132802","journal-title":"J Chem Inf Model"},{"issue":"7","key":"507_CR30","doi-asserted-by":"publisher","first-page":"3291","DOI":"10.1021\/acs.jcim.9b00334","volume":"59","author":"DD Nguyen","year":"2019","unstructured":"Nguyen DD, Wei G-W (2019) Agl-Score: algebraic graph learning score for protein-ligand binding scoring, ranking, docking, and screening. J Chem Inf Model 59(7):3291\u20133304","journal-title":"J Chem Inf Model"},{"issue":"14","key":"507_CR31","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(14):15956\u201315965","journal-title":"ACS Omega"},{"issue":"8","key":"507_CR32","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-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 35(8):1334\u20131341","journal-title":"Bioinformatics"},{"issue":"3","key":"507_CR33","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1002\/jcc.24667","volume":"38","author":"C Wang","year":"2017","unstructured":"Wang C, Zhang Y (2017) Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J Comput Chem 38(3):169\u2013177","journal-title":"J Comput Chem"},{"issue":"W1","key":"507_CR34","doi-asserted-by":"publisher","first-page":"W443","DOI":"10.1093\/nar\/gkv315","volume":"43","author":"S Salentin","year":"2015","unstructured":"Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M (2015) PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 43(W1):W443\u2013W447","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"507_CR35","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1002\/cmdc.201700505","volume":"13","author":"F Da Silva","year":"2018","unstructured":"Da Silva F, Desaphy J, Rognan D (2018) IChem: a versatile toolkit for detecting, comparing, and predicting protein\u2013ligand interactions. ChemMedChem 13(6):507\u2013510","journal-title":"ChemMedChem"},{"issue":"2","key":"507_CR36","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1021\/jm030331x","volume":"47","author":"Z Deng","year":"2004","unstructured":"Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein\u2212ligand binding interactions. J Med Chem 47(2):337\u2013344","journal-title":"J Med Chem"},{"issue":"5","key":"507_CR37","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1021\/ci900043r","volume":"49","author":"VI P\u00e9rez-Nueno","year":"2009","unstructured":"P\u00e9rez-Nueno VI, Rabal O, Borrell JI, Teixid\u00f3 J (2009) APIF: a new interaction fingerprint based on atom pairs and its application to virtual screening. J Chem Inf Model 49(5):1245\u20131260","journal-title":"J Chem Inf Model"},{"issue":"1","key":"507_CR38","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1021\/jm049312t","volume":"48","author":"C Chuaqui","year":"2005","unstructured":"Chuaqui C, Deng Z, Singh J (2005) Interaction profiles of protein kinase\u2014inhibitor complexes and their application to virtual screening. J Med Chem 48(1):121\u2013133","journal-title":"J Med Chem"},{"issue":"1","key":"507_CR39","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1021\/ci600342e","volume":"47","author":"G Marcou","year":"2007","unstructured":"Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47(1):195\u2013207","journal-title":"J Chem Inf Model"},{"issue":"1","key":"507_CR40","doi-asserted-by":"publisher","first-page":"e8554","DOI":"10.1371\/journal.pone.0008554","volume":"5","author":"M Chalopin","year":"2010","unstructured":"Chalopin M, Tesse A, Mart\u00ednez MC, Rognan D, Arnal J-F, Andriantsitohaina R (2010) Estrogen receptor alpha as a key target of red wine polyphenols action on the endothelium. PLoS ONE 5(1):e8554","journal-title":"PLoS ONE"},{"issue":"16","key":"507_CR41","doi-asserted-by":"publisher","first-page":"4978","DOI":"10.1021\/jm800710x","volume":"51","author":"C de Graaf","year":"2008","unstructured":"de Graaf C, Rognan D (2008) Selective structure-based virtual screening for full and partial agonists of the \u03922 adrenergic receptor. J Med Chem 51(16):4978\u20134985","journal-title":"J Med Chem"},{"issue":"4","key":"507_CR42","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1021\/ci300200r","volume":"53","author":"V Chupakhin","year":"2013","unstructured":"Chupakhin V, Marcou G, Baskin I, Varnek A, Rognan D (2013) Predicting ligand binding modes from neural networks trained on protein\u2013ligand interaction fingerprints. J Chem Inf Model 53(4):763\u2013772","journal-title":"J Chem Inf Model"},{"issue":"2","key":"507_CR43","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1021\/jm050381x","volume":"49","author":"Z Deng","year":"2006","unstructured":"Deng Z, Chuaqui C, Singh J (2006) Knowledge-based design of target-focused libraries using protein\u2212ligand interaction constraints. J Med Chem 49(2):490\u2013500","journal-title":"J Med Chem"},{"issue":"11","key":"507_CR44","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1021\/ci2003889","volume":"51","author":"JD Durrant","year":"2011","unstructured":"Durrant JD, McCammon JA (2011) NNScore 2.0: a neural-network receptor-ligand scoring function. J Chem Inf Model 51(11):2897\u20132903","journal-title":"J Chem Inf Model"},{"issue":"2","key":"507_CR45","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1038\/nmeth.2324","volume":"10","author":"H Lin","year":"2013","unstructured":"Lin H, Sassano MF, Roth BL, Shoichet BK (2013) A pharmacological organization of G protein-coupled receptors. Nat Methods 10(2):140","journal-title":"Nat Methods"},{"issue":"1","key":"507_CR46","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1042\/bj2990291","volume":"299","author":"EA Biessen","year":"1994","unstructured":"Biessen EA, Bakkeren HF, Beuting DM, Kuiper J, Van Berkel TJ (1994) Ligand size is a major determinant of high-affinity binding of fucose-and galactose-exposing (Lipo) proteins by the hepatic fucose receptor. Biochem J 299(1):291\u2013296","journal-title":"Biochem J"},{"issue":"8","key":"507_CR47","doi-asserted-by":"publisher","first-page":"2098","DOI":"10.1021\/ci200612f","volume":"52","author":"RD Smith","year":"2012","unstructured":"Smith RD, Engdahl AL, Dunbar JB Jr, Carlson HA (2012) Biophysical limits of protein-ligand binding. J Chem Inf Model 52(8):2098\u20132106","journal-title":"J Chem Inf Model"},{"issue":"1","key":"507_CR48","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/1656274.1656280","volume":"11","author":"MR Berthold","year":"2009","unstructured":"Berthold MR, Cebron N, Dill F, Gabriel TR, K\u00f6tter T, Meinl T, Ohl P, Thiel K, Wiswedel B (2009) KNIME-the Konstanz Information Miner: Version 2.0 and Beyond. AcM SIGKDD Explor Newsl 11(1):26\u201331","journal-title":"AcM SIGKDD Explor Newsl"},{"issue":"3","key":"507_CR49","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s10822-013-9644-8","volume":"27","author":"GM Sastry","year":"2013","unstructured":"Sastry GM, 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","journal-title":"J Comput Aided Mol Des"},{"issue":"9\u201310","key":"507_CR50","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s10822-005-9008-0","volume":"19","author":"A Varnek","year":"2005","unstructured":"Varnek A, Fourches D, Hoonakker F, Solov\u2019ev VP (2005) Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J Comput-Aided mol Des 19(9\u201310):693\u2013703","journal-title":"J Comput-Aided mol Des"},{"issue":"6","key":"507_CR51","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947\u20131958","journal-title":"J Chem Inf Comput Sci"},{"key":"507_CR52","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"1","key":"507_CR53","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.gpb.2017.07.003","volume":"16","author":"C Cao","year":"2018","unstructured":"Cao C, Liu F, Tan H, Song D, Shu W, Li W, Zhou Y, Bo X, Xie Z (2018) Deep learning and its applications in biomedicine. Genom Proteom Bioinform 16(1):17\u201332","journal-title":"Genom Proteom Bioinform"},{"issue":"3","key":"507_CR54","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1208\/s12248-018-0210-0","volume":"20","author":"Y Jing","year":"2018","unstructured":"Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS (2018) Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the Big Data Era. AAPS J 20(3):58","journal-title":"AAPS J"},{"key":"507_CR55","unstructured":"Chollet, F. Keras, 2015, GitHub. https:\/\/github.com\/fchollet\/keras"},{"issue":"4","key":"507_CR56","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1021\/jm061277y","volume":"50","author":"MJ Hartshorn","year":"2007","unstructured":"Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WT, Mortenson PN, Murray CW (2007) Diverse, high-quality test set for the validation of protein\u2212ligand docking performance. J Med Chem 50(4):726\u2013741","journal-title":"J Med Chem"},{"issue":"1","key":"507_CR57","doi-asserted-by":"publisher","first-page":"e1005929","DOI":"10.1371\/journal.pcbi.1005929","volume":"14","author":"Z Cang","year":"2018","unstructured":"Cang Z, Mu L, Wei G-W (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929","journal-title":"PLoS Comput Biol"},{"issue":"7","key":"507_CR58","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1021\/acs.jcim.7b00226","volume":"57","author":"DD Nguyen","year":"2017","unstructured":"Nguyen DD, Xiao T, Wang M, Wei G-W (2017) Rigidity strengthening: a mechanism for protein-ligand binding. J Chem Inf Model 57(7):1715\u20131721","journal-title":"J Chem Inf Model"},{"issue":"1","key":"507_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-015-0078-2","volume":"7","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 7(1):1\u20136","journal-title":"J Cheminform"},{"key":"507_CR60","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.engappai.2015.06.021","volume":"45","author":"MA Khamis","year":"2015","unstructured":"Khamis MA, Gomaa W (2015) Comparative assessment of machine-learning scoring functions on PDBbind 2013. Eng Appl Artif Intell 45:136\u2013151","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"507_CR61","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1021\/acs.jcim.8b00312","volume":"58","author":"T Gaillard","year":"2018","unstructured":"Gaillard T (2018) Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark. J Chem Inf Model 58(8):1697\u20131706","journal-title":"J Chem Inf Model"},{"issue":"6","key":"507_CR62","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1137\/130938517","volume":"44","author":"M Braverman","year":"2015","unstructured":"Braverman M (2015) Interactive information complexity. SIAM Journal on Computing 44(6):1698\u20131739","journal-title":"SIAM Journal on Computing"},{"issue":"3","key":"507_CR63","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(3):944\u2013955","journal-title":"J Chem Inf Model"},{"issue":"2","key":"507_CR64","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","volume":"59","author":"M Su","year":"2018","unstructured":"Su M, Yang Q, Du Y, Feng G, Liu Z, Li Y, Wang R (2018) Comparative assessment of scoring functions: the CASF-2016 update. J Chem Inf Model 59(2):895\u2013913","journal-title":"J Chem Inf Model"},{"issue":"1","key":"507_CR65","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/1472-6807-7-36","volume":"7","author":"J Adam","year":"2007","unstructured":"Adam J, Pokorn\u00e1 M, Sabin C, Mitchell EP, Imberty A, Wimmerov\u00e1 M (2007) Engineering of PA-IIL lectin from Pseudomonas Aeruginosa-unravelling the role of the specificity loop for sugar preference. BMC Struct Biol 7(1):36","journal-title":"BMC Struct Biol"},{"issue":"4","key":"507_CR66","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s00775-012-0873-4","volume":"17","author":"K Hanaya","year":"2012","unstructured":"Hanaya K, Suetsugu M, Saijo S, Yamato I, Aoki S (2012) Potent inhibition of dinuclear zinc (II) peptidase, an aminopeptidase from aeromonas proteolytica, by 8-quinolinol derivatives: inhibitor design based on Zn 2+ fluorophores, kinetic, and X-ray crystallographic study. J Biol Inorg Chem 17(4):517\u2013529","journal-title":"J Biol Inorg Chem"},{"issue":"4","key":"507_CR67","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuron.2005.01.031","volume":"45","author":"ML Mayer","year":"2005","unstructured":"Mayer ML (2005) Crystal structures of the GluR5 and GluR6 ligand binding cores: molecular mechanisms underlying kainate receptor selectivity. Neuron 45(4):539\u2013552","journal-title":"Neuron"},{"issue":"13","key":"507_CR68","doi-asserted-by":"publisher","first-page":"3381","DOI":"10.1021\/jm030640n","volume":"47","author":"KP Madauss","year":"2004","unstructured":"Madauss KP, Deng S-J, Austin RJ, Lambert MH, McLay I, Pritchard J, Short SA, Stewart EL, Uings IJ, Williams SP (2004) Progesterone receptor ligand binding pocket flexibility: crystal structures of the norethindrone and mometasone furoate complexes. J Med Chem 47(13):3381\u20133387","journal-title":"J Med Chem"},{"issue":"1","key":"507_CR69","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1002\/chem.201102571","volume":"18","author":"LM Salonen","year":"2012","unstructured":"Salonen LM, Holland MC, Kaib PS, Haap W, Benz J, Mary J-L, Kuster O, Schweizer WB, Banner DW, Diederich F (2012) Molecular recognition at the active site of factor Xa: cation\u2013\u03c0 interactions, stacking on planar peptide surfaces, and replacement of structural water. Chem Eur J 18(1):213\u2013222","journal-title":"Chem Eur J"},{"issue":"3","key":"507_CR70","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1111\/j.1742-4658.2004.04511.x","volume":"272","author":"CL Jenkins","year":"2005","unstructured":"Jenkins CL, Thiyagarajan N, Sweeney RY, Guy MP, Kelemen BR, Acharya KR, Raines RT (2005) Binding of non-natural 3\u2032-nucleotides to ribonuclease A. FEBS J 272(3):744\u2013755","journal-title":"FEBS J"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00507-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00507-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00507-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T19:48:47Z","timestamp":1698090527000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-021-00507-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["507"],"URL":"https:\/\/doi.org\/10.1186\/s13321-021-00507-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-74202\/v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]},"assertion":[{"value":"7 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 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 confirm that this article content has no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"28"}}