{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:40:50Z","timestamp":1782931250993,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,5,20]],"date-time":"2015-05-20T00:00:00Z","timestamp":1432080000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2015,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s13321-015-0074-6","type":"journal-article","created":{"date-parts":[[2015,5,19]],"date-time":"2015-05-19T15:36:15Z","timestamp":1432049775000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization"],"prefix":"10.1186","volume":"7","author":[{"given":"Tamer M","family":"Ibrahim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias R","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank M","family":"Boeckler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2015,5,20]]},"reference":[{"key":"74_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/nrd3139","volume":"9","author":"G Schneider","year":"2010","unstructured":"Schneider G. Virtual screening: an endless staircase? Nat Rev Drug Discov. 2010;9:273\u20136.","journal-title":"Nat Rev Drug Discov"},{"key":"74_CR2","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1021\/ci200528d","volume":"52","author":"T Scior","year":"2012","unstructured":"Scior T, Bender A, Tresadern G, Medina-Franco JL, Martinez-Mayorga K, Langer T, et al. Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model. 2012;52:867\u201381.","journal-title":"J Chem Inf Model"},{"key":"74_CR3","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1021\/jm0300173","volume":"46","author":"M Schapira","year":"2003","unstructured":"Schapira M, Abagyan R, Totrov M. Nuclear hormone receptor targeted virtual screening. J Med Chem. 2003;46:3045\u201359.","journal-title":"J Med Chem"},{"key":"74_CR4","doi-asserted-by":"publisher","first-page":"2192","DOI":"10.1021\/ci300073m","volume":"52","author":"DN Santiago","year":"2012","unstructured":"Santiago DN, Pevzner Y, Durand AA, Tran M, Scheerer RR, Daniel K, et al. Virtual target screening: validation using kinase inhibitors. J Chem Inf Model. 2012;52:2192\u2013203.","journal-title":"J Chem Inf Model"},{"key":"74_CR5","doi-asserted-by":"publisher","first-page":"10360","DOI":"10.1073\/pnas.0805326105","volume":"105","author":"FM Boeckler","year":"2008","unstructured":"Boeckler FM, Joerger AC, Jaggi G, Rutherford TJ, Veprintsev DB, Fersht AR. Targeted rescue of a destabilized mutant of p53 by an in silico screened drug. Proc Natl Acad Sci U S A. 2008;105:10360\u20135.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"74_CR6","doi-asserted-by":"publisher","first-page":"16906","DOI":"10.1073\/pnas.1215060109","volume":"109","author":"SM Vogel","year":"2012","unstructured":"Vogel SM, Bauer MR, Joerger AC, Wilcken R, Brandt T, Veprintsev DB, et al. Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2. Proc Natl Acad Sci U S A. 2012;109:16906\u201310.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"74_CR7","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1517\/17460441.2013.761204","volume":"8","author":"S Kar","year":"2013","unstructured":"Kar S, Roy K. How far can virtual screening take us in drug discovery? Expert Opin Drug Discov. 2013;8:245\u201361.","journal-title":"Expert Opin Drug Discov"},{"key":"74_CR8","first-page":"397","volume":"12","author":"H Koppen","year":"2009","unstructured":"Koppen H. Virtual screening - what does it give us? Curr Opin Drug Discov Devel. 2009;12:397\u2013407.","journal-title":"Curr Opin Drug Discov Devel"},{"key":"74_CR9","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.2174\/1381612043452721","volume":"10","author":"T Hou","year":"2004","unstructured":"Hou T, Xu X. Recent development and application of virtual screening in drug discovery: an overview. Curr Pharm Des. 2004;10:1011\u201333.","journal-title":"Curr Pharm Des"},{"key":"74_CR10","doi-asserted-by":"publisher","first-page":"7323","DOI":"10.1021\/jm901191q","volume":"52","author":"M Okamoto","year":"2009","unstructured":"Okamoto M, Takayama K, Shimizu T, Ishida K, Takahashi O, Furuya T. Identification of death-associated protein kinases inhibitors using structure-based virtual screening. J Med Chem. 2009;52:7323\u20137.","journal-title":"J Med Chem"},{"key":"74_CR11","doi-asserted-by":"publisher","first-page":"3145","DOI":"10.1021\/jm7014777","volume":"51","author":"R Kiss","year":"2008","unstructured":"Kiss R, Kiss B, Konczol A, Szalai F, Jelinek I, Laszlo V, et al. Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening. J Med Chem. 2008;51:3145\u201353.","journal-title":"J Med Chem"},{"key":"74_CR12","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1021\/jm061389p","volume":"50","author":"E Kellenberger","year":"2007","unstructured":"Kellenberger E, Springael JY, Parmentier M, Hachet-Haas M, Galzi JL, Rognan D. Identification of nonpeptide CCR5 receptor agonists by structure-based virtual screening. J Med Chem. 2007;50:1294\u2013303.","journal-title":"J Med Chem"},{"key":"74_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1208\/s12248-012-9322-0","volume":"14","author":"T Cheng","year":"2012","unstructured":"Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 2012;14:133\u201341.","journal-title":"AAPS J"},{"key":"74_CR14","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.2174\/138620709789824682","volume":"12","author":"BO Villoutreix","year":"2009","unstructured":"Villoutreix BO, Eudes R, Miteva MA. Structure-based virtual ligand screening: recent success stories. Comb Chem High Throughput Screen. 2009;12:1000\u201316.","journal-title":"Comb Chem High Throughput Screen"},{"key":"74_CR15","doi-asserted-by":"publisher","first-page":"5851","DOI":"10.1021\/jm060999m","volume":"49","author":"AR Leach","year":"2006","unstructured":"Leach AR, Shoichet BK, Peishoff CE. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J Med Chem. 2006;49:5851\u20135.","journal-title":"J Med Chem"},{"key":"74_CR16","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1038\/nrd1549","volume":"3","author":"DB Kitchen","year":"2004","unstructured":"Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3:935\u201349.","journal-title":"Nat Rev Drug Discov"},{"key":"74_CR17","doi-asserted-by":"publisher","first-page":"12899","DOI":"10.1039\/c0cp00151a","volume":"12","author":"SY Huang","year":"2010","unstructured":"Huang SY, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys. 2010;12:12899\u2013908.","journal-title":"Phys Chem Chem Phys"},{"key":"74_CR18","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.drudis.2009.03.013","volume":"14","author":"MH Seifert","year":"2009","unstructured":"Seifert MH. Targeted scoring functions for virtual screening. Drug Discov Today. 2009;14:562\u20139.","journal-title":"Drug Discov Today"},{"key":"74_CR19","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.2174\/1381612811319120005","volume":"19","author":"JC Wang","year":"2013","unstructured":"Wang JC, Lin JH. Scoring functions for prediction of protein-ligand interactions. Curr Pharm Des. 2013;19:2174\u201382.","journal-title":"Curr Pharm Des"},{"key":"74_CR20","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1016\/S1359-6446(02)02483-2","volume":"7","author":"PD Lyne","year":"2002","unstructured":"Lyne PD. Structure-based virtual screening: an overview. Drug Discov Today. 2002;7:1047\u201355.","journal-title":"Drug Discov Today"},{"key":"74_CR21","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. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 - a public library of challenging docking benchmark sets. J Chem Inf Model. 2013;53:1447\u201362.","journal-title":"J Chem Inf Model"},{"key":"74_CR22","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. Directory of useful decoys, enhanced (DUD-E) - better ligands and decoys for better benchmarking. J Med Chem. 2012;55:6582\u201394.","journal-title":"J Med Chem"},{"key":"74_CR23","doi-asserted-by":"publisher","first-page":"6789","DOI":"10.1021\/jm0608356","volume":"49","author":"N Huang","year":"2006","unstructured":"Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem. 2006;49:6789\u2013801.","journal-title":"J Med Chem"},{"key":"74_CR24","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.1021\/ci2001549","volume":"51","author":"SM Vogel","year":"2011","unstructured":"Vogel SM, Bauer MR, Boeckler FM. DEKOIS: demanding evaluation kits for objective in silico screening\u2013a versatile tool for benchmarking docking programs and scoring functions. J Chem Inf Model. 2011;51:2650\u201365.","journal-title":"J Chem Inf Model"},{"key":"74_CR25","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer SG, Baumann K. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J Chem Inf Model. 2009;49:169\u201384.","journal-title":"J Chem Inf Model"},{"key":"74_CR26","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1021\/ci200611t","volume":"52","author":"A Barman","year":"2012","unstructured":"Barman A, Prabhakar R. Protonation states of the catalytic dyad of beta-secretase (BACE1) in the presence of chemically diverse inhibitors: a molecular docking study. J Chem Inf Model. 2012;52:1275\u201387.","journal-title":"J Chem Inf Model"},{"key":"74_CR27","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1021\/ci800420z","volume":"49","author":"T ten Brink","year":"2009","unstructured":"ten Brink T, Exner TE. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J Chem Inf Model. 2009;49:1535\u201346.","journal-title":"J Chem Inf Model"},{"key":"74_CR28","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1021\/ci700223p","volume":"47","author":"T Polgar","year":"2007","unstructured":"Polgar T, Magyar C, Simon I, Keseru GM. Impact of ligand protonation on virtual screening against beta-secretase (BACE1). J Chem Inf Model. 2007;47:2366\u201373.","journal-title":"J Chem Inf Model"},{"key":"74_CR29","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1021\/ci900364w","volume":"49","author":"T Kalliokoski","year":"2009","unstructured":"Kalliokoski T, Salo HS, Lahtela-Kakkonen M, Poso A. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J Chem Inf Model. 2009;49:2742\u20138.","journal-title":"J Chem Inf Model"},{"key":"74_CR30","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s10822-010-9385-x","volume":"24","author":"T ten Brink","year":"2010","unstructured":"ten Brink T, Exner TE. pK(a) based protonation states and microspecies for protein-ligand docking. J Comput Aided Mol Des. 2010;24:935\u201342.","journal-title":"J Comput Aided Mol Des"},{"key":"74_CR31","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1021\/ci200598m","volume":"52","author":"M Feher","year":"2012","unstructured":"Feher M, Williams CI. Numerical errors and chaotic behavior in docking simulations. J Chem Inf Model. 2012;52:724\u201338.","journal-title":"J Chem Inf Model"},{"key":"74_CR32","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1021\/ci9000629","volume":"49","author":"M Feher","year":"2009","unstructured":"Feher M, Williams CI. Effect of input differences on the results of docking calculations. J Chem Inf Model. 2009;49:1704\u201314.","journal-title":"J Chem Inf Model"},{"key":"74_CR33","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1002\/prot.20149","volume":"57","author":"E Kellenberger","year":"2004","unstructured":"Kellenberger E, Rodrigo J, Muller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins. 2004;57:225\u201342.","journal-title":"Proteins"},{"key":"74_CR34","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1002\/prot.20088","volume":"56","author":"E Perola","year":"2004","unstructured":"Perola E, Walters WP, Charifson PS. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins. 2004;56:235\u201349.","journal-title":"Proteins"},{"key":"74_CR35","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10822-007-9154-7","volume":"22","author":"CI Williams","year":"2008","unstructured":"Williams CI, Feher M. The effect of numerical error on the reproducibility of molecular geometry optimizations. J Comput Aided Mol Des. 2008;22:39\u201351.","journal-title":"J Comput Aided Mol Des"},{"issue":"Suppl 1","key":"74_CR36","doi-asserted-by":"publisher","first-page":"O24","DOI":"10.1186\/1758-2946-6-S1-O24","volume":"6","author":"FM Boeckler","year":"2014","unstructured":"Boeckler FM, Bauer MR, Ibrahim TM, Vogel SM. Use of DEKOIS 2.0 to gain insights for virtual screening [abstract]. J Cheminform. 2014;6 Suppl 1:O24.","journal-title":"J Cheminform"},{"key":"74_CR37","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. Managing bias in ROC curves. J Comput Aided Mol Des. 2008;22:141\u20136.","journal-title":"J Comput Aided Mol Des"},{"key":"74_CR38","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1081\/RRS-120026975","volume":"23","author":"P Pospisil","year":"2003","unstructured":"Pospisil P, Ballmer P, Scapozza L, Folkers G. Tautomerism in computer-aided drug design. J Recept Signal Transduct Res. 2003;23:361\u201371.","journal-title":"J Recept Signal Transduct Res"},{"key":"74_CR39","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1021\/jm030209y","volume":"47","author":"JA Erickson","year":"2004","unstructured":"Erickson JA, Jalaie M, Robertson DH, Lewis RA, Vieth M. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem. 2004;47:45\u201355.","journal-title":"J Med Chem"},{"key":"74_CR40","doi-asserted-by":"publisher","first-page":"8718","DOI":"10.1021\/bi049480n","volume":"43","author":"R Natesh","year":"2004","unstructured":"Natesh R, Schwager SL, Evans HR, Sturrock ED, Acharya KR. Structural details on the binding of antihypertensive drugs captopril and enalaprilat to human testicular angiotensin I-converting enzyme. Biochemistry. 2004;43:8718\u201324.","journal-title":"Biochemistry"},{"key":"74_CR41","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1021\/ci020055f","volume":"43","author":"Y Pan","year":"2003","unstructured":"Pan Y, Huang N, Cho S, MacKerell Jr AD. Consideration of molecular weight during compound selection in virtual target-based database screening. J Chem Inf Comput Sci. 2003;43:267\u201372.","journal-title":"J Chem Inf Comput Sci"},{"key":"74_CR42","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1021\/ci600471m","volume":"47","author":"G Carta","year":"2007","unstructured":"Carta G, Knox AJ, Lloyd DG. Unbiasing scoring functions: a new normalization and rescoring strategy. J Chem Inf Model. 2007;47:1564\u201371.","journal-title":"J Chem Inf Model"},{"key":"74_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1021\/ci800298z","volume":"49","author":"O Korb","year":"2009","unstructured":"Korb O, Stutzle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model. 2009;49:84\u201396.","journal-title":"J Chem Inf Model"},{"key":"74_CR44","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s10822-012-9551-4","volume":"26","author":"JW Liebeschuetz","year":"2012","unstructured":"Liebeschuetz JW, Cole JC, Korb O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J Comput Aided Mol Des. 2012;26:737\u201348.","journal-title":"J Comput Aided Mol Des"},{"key":"74_CR45","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, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47:1739\u201349.","journal-title":"J Med Chem"},{"key":"74_CR46","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, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem. 2004;47:1750\u20139.","journal-title":"J Med Chem"},{"key":"74_CR47","volume-title":"Suite 2010","author":"Protein Preparation Wizard","year":"2010","unstructured":"Protein Preparation Wizard. Suite 2010. New York, NY: Schr\u00f6dinger, LLC; 2010."},{"key":"74_CR48","unstructured":"Epik, version. 2.1. New York, NY: Schr\u00f6dinger, LLC; 2010."},{"key":"74_CR49","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s10822-007-9133-z","volume":"21","author":"JC Shelley","year":"2007","unstructured":"Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M. Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules. J Comput-Aided Mol Des. 2007;21:681\u201391.","journal-title":"J Comput-Aided Mol Des"},{"key":"74_CR50","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. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267:727\u201348.","journal-title":"J Mol Biol"},{"key":"74_CR51","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S0022-2836(95)80037-9","volume":"245","author":"G Jones","year":"1995","unstructured":"Jones G, Willett P, Glen RC. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol. 1995;245:43\u201353.","journal-title":"J Mol Biol"},{"key":"74_CR52","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/BF00124324","volume":"9","author":"G Jones","year":"1995","unstructured":"Jones G, Willett P, Glen RC. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput-Aided Mol Des. 1995;9:532\u201349.","journal-title":"J Comput-Aided Mol Des"},{"key":"74_CR53","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, et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem. 2007;50:726\u201341.","journal-title":"J Med Chem"},{"key":"74_CR54","unstructured":"Ligprep, version 2.4. New York, NY: Schr\u00f6dinger, LLC: 2010."}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0074-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-015-0074-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0074-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0074-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T13:27:11Z","timestamp":1630589231000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-015-0074-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,20]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,12]]}},"alternative-id":["74"],"URL":"https:\/\/doi.org\/10.1186\/s13321-015-0074-6","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,5,20]]},"assertion":[{"value":"12 January 2015","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2015","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2015","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"21"}}