{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:04:52Z","timestamp":1778857492962,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T00:00:00Z","timestamp":1565136000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T00:00:00Z","timestamp":1565136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GD Frontier & Key Techn, Innovation Program","award":["2015B010109004"],"award-info":[{"award-number":["2015B010109004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1611261"],"award-info":[{"award-number":["U1611261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP180102060"],"award-info":[{"award-number":["DP180102060"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"crossref","award":["1121629"],"award-info":[{"award-number":["1121629"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772566"],"award-info":[{"award-number":["61772566"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s13321-019-0373-4","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:03:03Z","timestamp":1565182983000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["DLIGAND2: an improved knowledge-based energy function for protein\u2013ligand interactions using the distance-scaled, finite, ideal-gas reference state"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8746-9917","authenticated-orcid":false,"given":"Pin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9575-5745","authenticated-orcid":false,"given":"Yaobin","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5315-3375","authenticated-orcid":false,"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6832-0519","authenticated-orcid":false,"given":"Yunfei","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5455-9374","authenticated-orcid":false,"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6653-1451","authenticated-orcid":false,"given":"Hui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9134-536X","authenticated-orcid":false,"given":"Huiying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9958-5699","authenticated-orcid":false,"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6782-2813","authenticated-orcid":false,"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,7]]},"reference":[{"key":"373_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/nature19112","volume":"537","author":"A Manglik","year":"2016","unstructured":"Manglik A, Lin H, Aryal DK, Mccorvy JD, Dengler D, Corder G, Levit A, Kling RC, Bernat V, HuBner H (2016) Structure-based discovery of opioid analgesics with reduced side effects. Nature 537:185\u2013190","journal-title":"Nature"},{"key":"373_CR2","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1021\/ci5000196","volume":"54","author":"KR Valasani","year":"2014","unstructured":"Valasani KR, Vangavaragu JR, Day VW, Yan SS (2014) Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. J Chem Inf Model 54:902\u2013912","journal-title":"J Chem Inf Model"},{"key":"373_CR3","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1038\/s41598-017-02023-5","volume":"7","author":"AN Singh","year":"2017","unstructured":"Singh AN, Baruah MM, Sharma N (2017) Structure based docking studies towards exploring potential anti-androgen activity of selected phytochemicals against Prostate Cancer. Sci Rep 7:1955","journal-title":"Sci Rep"},{"key":"373_CR4","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\u2013ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964\u201312975","journal-title":"Phys Chem Chem Phys"},{"key":"373_CR5","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1021\/ci500081m","volume":"54","author":"Y Li","year":"2014","unstructured":"Li Y, Han L, Liu Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 54:1717\u20131736","journal-title":"J Chem Inf Model"},{"key":"373_CR6","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1021\/ci500080q","volume":"54","author":"Y Li","year":"2014","unstructured":"Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. J Chem Inf Model 54:1700\u20131716","journal-title":"J Chem Inf Model"},{"key":"373_CR7","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1021\/ci500731a","volume":"55","author":"J Liu","year":"2015","unstructured":"Liu J, Wang R (2015) On classification of current scoring functions. J Chem Inf Model 55:475\u2013482","journal-title":"J Chem Inf Model"},{"key":"373_CR8","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1093\/protein\/7.3.385","volume":"7","author":"J Aqvist","year":"1994","unstructured":"Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385\u2013391","journal-title":"Protein Eng"},{"key":"373_CR9","first-page":"1242","volume":"25","author":"AF Martin","year":"2010","unstructured":"Martin AF, Brandsdal BRO, Johan A (2010) Binding affinity prediction with different force fields: examination of the linear interaction energy method. J Comput Chem 25:1242\u20131254","journal-title":"J Comput Chem"},{"key":"373_CR10","doi-asserted-by":"publisher","first-page":"10667","DOI":"10.1021\/j100026a034","volume":"99","author":"HA Carlson","year":"1995","unstructured":"Carlson HA, Jorgensen WL (1995) An extended linear response method for determining free energies of hydration. J Phys Chem 99:10667\u201310673","journal-title":"J Phys Chem"},{"key":"373_CR11","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 51:69\u201382","journal-title":"J Chem Inf Model"},{"key":"373_CR12","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1002\/jcc.21666","volume":"32","author":"T Hou","year":"2011","unstructured":"Hou T, Wang J, Li Y, Wei W (2011) Assessing the performance of the MM\/PBSA and MM\/GBSA methods: II. The accuracy of ranking poses generated from docking. J Comput Chem 32:866\u2013877","journal-title":"J Comput Chem"},{"key":"373_CR13","doi-asserted-by":"publisher","first-page":"16719","DOI":"10.1039\/C4CP01388C","volume":"16","author":"H Sun","year":"2014","unstructured":"Sun H, Li Y, Tian S, Xu L, Hou T (2014) Assessing the performance of MM\/PBSA and MM\/GBSA methods. 4. Accuracies of MM\/PBSA and MM\/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys 16:16719\u201316729","journal-title":"Phys Chem Chem Phys"},{"key":"373_CR14","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1023\/A:1007996124545","volume":"11","author":"MD Eldridge","year":"1997","unstructured":"Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425\u2013445","journal-title":"J Comput Aided Mol Des"},{"key":"373_CR15","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1023\/A:1008040323669","volume":"12","author":"CW Murray","year":"1998","unstructured":"Murray CW, Auton TR, Eldridge MD (1998) Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand\u2013receptor binding affinities and the use of Bayesian regression to improve the quality of the model. J Comput Aided Mol Des 12:503\u2013519","journal-title":"J Comput Aided Mol Des"},{"key":"373_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1023\/A:1016357811882","volume":"16","author":"R Wang","year":"2002","unstructured":"Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11\u201326","journal-title":"J Comput Aided Mol Des"},{"key":"373_CR17","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 47:1739\u20131749","journal-title":"J Med Chem"},{"key":"373_CR18","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 47:1750\u20131759","journal-title":"J Med Chem"},{"key":"373_CR19","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\u2013ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169\u20131175","journal-title":"Bioinformatics"},{"key":"373_CR20","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1021\/ci300493w","volume":"53","author":"GB Li","year":"2013","unstructured":"Li GB, Yang LL, Wang WJ, Li LL, Yang SY (2013) ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein\u2013ligand interactions. J Chem Inf Model 53:592\u2013600","journal-title":"J Chem Inf Model"},{"key":"373_CR21","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1021\/ci100490w","volume":"51","author":"L Li","year":"2011","unstructured":"Li L, Khanna M, Jo I, Wang F, Ashpole NM, Hudmon A, Meroueh SO (2011) Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model 51:755\u2013759","journal-title":"J Chem Inf Model"},{"key":"373_CR22","doi-asserted-by":"crossref","unstructured":"Zheng S, Li Y, Chen S, Xu J, Yang Y (2019) Predicting drug protein interaction using quasi-visual question answering system. http:\/\/bioRxiv.org\/abs\/588178","DOI":"10.1101\/588178"},{"key":"373_CR23","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1021\/ci500406k","volume":"54","author":"J Gabel","year":"2014","unstructured":"Gabel J, Desaphy J, Rognan D (2014) Beware of machine learning-based scoring functions-on the danger of developing black boxes. J Chem Inf Model 54:2807\u20132815","journal-title":"J Chem Inf Model"},{"key":"373_CR24","doi-asserted-by":"publisher","first-page":"11733","DOI":"10.1021\/ja960751u","volume":"118","author":"RS DeWitte","year":"1996","unstructured":"DeWitte RS, Shakhnovich EI (1996) SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11733\u201311744","journal-title":"J Am Chem Soc"},{"key":"373_CR25","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1021\/ar970146b","volume":"35","author":"BA Grzybowski","year":"2002","unstructured":"Grzybowski BA, Ishchenko AV, Shimada J, Shakhnovich EI (2002) From knowledge-based potentials to combinatorial lead design in silico. Acc Chem Res 35:261\u2013269","journal-title":"Acc Chem Res"},{"key":"373_CR26","doi-asserted-by":"publisher","first-page":"6296","DOI":"10.1021\/jm050436v","volume":"48","author":"HFG Velec","year":"2005","unstructured":"Velec HFG, Gohlke H, Klebe G (2005) DrugScoreCSDKnowledge-Based Scoring Function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48:6296\u20136303","journal-title":"J Med Chem"},{"key":"373_CR27","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1002\/jcc.20504","volume":"27","author":"S Huang","year":"2006","unstructured":"Huang S, Zou X (2006) An iterative knowledge-based scoring function to predict protein\u2013ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27:1866\u20131875","journal-title":"J Comput Chem"},{"key":"373_CR28","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1002\/jcc.20505","volume":"27","author":"S Huang","year":"2006","unstructured":"Huang S, Zou X (2006) An iterative knowledge-based scoring function to predict protein\u2013ligand interactions: II. Validation of the scoring function. J Comput Chem 27:1876\u20131882","journal-title":"J Comput Chem"},{"key":"373_CR29","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1002\/prot.20588","volume":"61","author":"WTM Mooij","year":"2005","unstructured":"Mooij WTM, Verdonk ML (2005) General and targeted statistical potentials for protein\u2013ligand interactions. Proteins 61:272\u2013287","journal-title":"Proteins"},{"key":"373_CR30","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1021\/ci200274q","volume":"51","author":"G Neudert","year":"2011","unstructured":"Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein\u2013ligand complexes. J Chem Inf Model 51:2731\u20132745","journal-title":"J Chem Inf Model"},{"key":"373_CR31","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1021\/acs.jcim.6b00610","volume":"57","author":"T Debroise","year":"2017","unstructured":"Debroise T, Shakhnovich EI, Ch\u00e9ron N (2017) A hybrid knowledge-based and empirical scoring function for protein\u2013ligand interaction: SMoG2016. J Chem Inf Model 57:584\u2013593","journal-title":"J Chem Inf Model"},{"key":"373_CR32","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1021\/ci9002987","volume":"50","author":"S Huang","year":"2010","unstructured":"Huang S, Zou X (2010) Inclusion of solvation and entropy in the knowledge-based scoring function for protein\u2013ligand interactions. J Chem Inf Model 50:262\u2013273","journal-title":"J Chem Inf Model"},{"key":"373_CR33","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.1110\/ps.0217002","volume":"11","author":"H Zhou","year":"2002","unstructured":"Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714\u20132726","journal-title":"Protein Sci"},{"key":"373_CR34","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1110\/ps.033480.107","volume":"17","author":"Y Yang","year":"2008","unstructured":"Yang Y, Zhou Y (2008) Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions. Protein Sci 17:1212\u20131219","journal-title":"Protein Sci"},{"key":"373_CR35","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1093\/bioinformatics\/btq295","volume":"26","author":"H Zhao","year":"2010","unstructured":"Zhao H, Yang Y, Zhou Y (2010) Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function. Bioinformatics 26:1857\u20131863","journal-title":"Bioinformatics"},{"key":"373_CR36","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1093\/nar\/gkq1266","volume":"39","author":"H Zhao","year":"2011","unstructured":"Zhao H, Yang Y, Zhou Y (2011) Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets. Nucleic Acids Res 39:3017\u20133025","journal-title":"Nucleic Acids Res"},{"key":"373_CR37","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1002\/jcc.23730","volume":"35","author":"H Zhao","year":"2014","unstructured":"Zhao H, Yang Y, von Itzstein M, Zhou Y (2014) Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction. J Comput Chem 35:2177\u20132183","journal-title":"J Comput Chem"},{"key":"373_CR38","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1021\/jm049314d","volume":"48","author":"C Zhang","year":"2005","unstructured":"Zhang C, Liu S, Zhu QQ, Zhou YQ (2005) A knowledge-based energy function for protein\u2013ligand, protein\u2013protein, and protein\u2013DNA complexes. J Med Chem 48:2325\u20132335","journal-title":"J Med Chem"},{"key":"373_CR39","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1093\/bioinformatics\/bty481","volume":"34","author":"J Hanson","year":"2018","unstructured":"Hanson J, Paliwal K, Litfin T, Yang Y, Zhou Y (2018) Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. Bioinformatics 34:4039\u20134045","journal-title":"Bioinformatics"},{"key":"373_CR40","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1002\/prot.22384","volume":"76","author":"B Xu","year":"2010","unstructured":"Xu B, Yang Y, Liang H, Zhou Y (2010) An all-atom knowledge-based energy function for protein\u2013DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles. Proteins Struct Funct Bioinform 76:718\u2013730","journal-title":"Proteins Struct Funct Bioinform"},{"key":"373_CR41","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1021\/jm030580l","volume":"47","author":"R Wang","year":"2004","unstructured":"Wang R, Fang X, Yipin LuA, Wang S (2004) The PDBbind database: collection of binding affinities for protein\u2013ligand complexes with known three-dimensional structures. J Med Chem 47:2977\u20132980","journal-title":"J Med Chem"},{"key":"373_CR42","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 30:2785\u20132791","journal-title":"J Comput Chem"},{"key":"373_CR43","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 31:455\u2013461","journal-title":"J Comput Chem"},{"issue":"4","key":"373_CR44","doi-asserted-by":"publisher","first-page":"e1003571","DOI":"10.1371\/journal.pcbi.1003571","volume":"10","author":"S Ruizcarmona","year":"2014","unstructured":"Ruizcarmona S, Alvarezgarcia D, Foloppe N, Garmendiadoval 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","journal-title":"PLoS Comput Biol"},{"key":"373_CR45","doi-asserted-by":"publisher","first-page":"5721","DOI":"10.1016\/j.bmcl.2013.08.009","volume":"23","author":"H Zhao","year":"2013","unstructured":"Zhao H, Caflisch A (2013) Discovery of ZAP70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics. Bioorg Med Chem Lett 23:5721\u20135726","journal-title":"Bioorg Med Chem Lett"},{"key":"373_CR46","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1021\/jp506555w","volume":"119","author":"L Jiang","year":"2015","unstructured":"Jiang L, Rizzo RC (2015) Pharmacophore-based similarity scoring for DOCK. J Phys Chem B 119:1083\u20131102","journal-title":"J Phys Chem B"},{"key":"373_CR47","doi-asserted-by":"crossref","unstructured":"Li H, Leung KS, Wong MH (2012) idock: a multithreaded virtual screening tool for flexible ligand docking. In: IEEE symposium on computational intelligence in bioinformatics & computational biology. pp 77\u201384","DOI":"10.1109\/CIBCB.2012.6217214"},{"key":"373_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10822-017-0030-9","volume":"31","author":"M Baek","year":"2017","unstructured":"Baek M, Shin WH, Chung HW, Seok C (2017) GalaxyDock BP2 score: a hybrid scoring function for accurate protein\u2013ligand docking. J Comput Aided Mol Des 31:1\u201314","journal-title":"J Comput Aided Mol Des"},{"key":"373_CR49","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1002\/jcc.23438","volume":"34","author":"WH Shin","year":"2013","unstructured":"Shin WH, Kim JK, Kim DS, Seok C (2013) GalaxyDock2: protein\u2013ligand docking using beta-complex and global optimization. J Comput Chem 34:2647\u20132656","journal-title":"J Comput Chem"},{"key":"373_CR50","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1002\/prot.20035","volume":"55","author":"JM Yang","year":"2004","unstructured":"Yang JM, Chen CC (2004) GEMDOCK: a generic evolutionary method for molecular docking. Proteins Struct Funct Bioinform 55:288\u2013304","journal-title":"Proteins Struct Funct Bioinform"},{"key":"373_CR51","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","journal-title":"J Med Chem"},{"key":"373_CR52","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1093\/bioinformatics\/btw829","volume":"2017","author":"T Litfin","year":"2017","unstructured":"Litfin T, Zhou YQ, Yang YD (2017) SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library. Bioinformatics 2017:1238\u20131240","journal-title":"Bioinformatics"},{"key":"373_CR53","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1002\/jcc.24380","volume":"37","author":"Y Yang","year":"2016","unstructured":"Yang Y, Zhan J, Zhou Y (2016) SPOT-ligand: fast and effective structure-based virtual screening by binding homology search according to ligand and receptor similarity. J Comput Chem 37:1734\u20131739","journal-title":"J Comput Chem"},{"key":"373_CR54","doi-asserted-by":"publisher","first-page":"46710","DOI":"10.1038\/srep46710","volume":"7","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 7:46710","journal-title":"Sci Rep"},{"key":"373_CR55","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 53:1447\u20131462","journal-title":"J Chem Inf Model"},{"key":"373_CR56","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","volume":"25","author":"SF Altschul","year":"1997","unstructured":"Altschul SF, Madden TL, Sch\u00e4ffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389\u20133402","journal-title":"Nucleic Acids Res"},{"key":"373_CR57","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":"373_CR58","first-page":"169","volume":"2017","author":"C Wang","year":"2016","unstructured":"Wang C, Zhang Y (2016) Improving scoring-docking-screening powers of protein\u2013ligand scoring functions using random forest. J Comput Chem 2017:169\u2013177","journal-title":"J Comput Chem"},{"key":"373_CR59","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1186\/s12859-016-1169-4","volume":"17","author":"H Li","year":"2016","unstructured":"Li H, Leung KS, Wong MH, Ballester PJ (2016) Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinform 17:308","journal-title":"BMC Bioinform"},{"key":"373_CR60","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1016\/j.jmb.2016.01.012","volume":"428","author":"L Folkman","year":"2016","unstructured":"Folkman L, Stantic B, Sattar A, Zhou Y (2016) EASE-MM: sequence-based prediction of mutation-induced stability changes with feature-based multiple models. J Mol Biol 428:1394\u20131405","journal-title":"J Mol Biol"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0373-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-019-0373-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0373-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T19:40:59Z","timestamp":1721590859000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-019-0373-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,7]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["373"],"URL":"https:\/\/doi.org\/10.1186\/s13321-019-0373-4","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,7]]},"assertion":[{"value":"2 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"52"}}