{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:52:36Z","timestamp":1780534356382,"version":"3.54.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM124270"],"award-info":[{"award-number":["R01GM124270"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30CA008748"],"award-info":[{"award-number":["P30CA008748"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM121505"],"award-info":[{"award-number":["R01GM121505"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CHE 1352608"],"award-info":[{"award-number":["CHE 1352608"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s10822-020-00362-6","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T05:02:59Z","timestamp":1609736579000},"page":"131-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6789-952X","authenticated-orcid":false,"given":"Mehtap","family":"I\u015f\u0131k","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-0613","authenticated-orcid":false,"given":"Ari\u00ebn S.","family":"Rustenburg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7693-2013","authenticated-orcid":false,"given":"Andrea","family":"Rizzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1120-5776","authenticated-orcid":false,"given":"M. R.","family":"Gunner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-5533","authenticated-orcid":false,"given":"David L.","family":"Mobley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0542-119X","authenticated-orcid":false,"given":"John D.","family":"Chodera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"issue":"2","key":"362_CR1","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1039\/C2CS35348B","volume":"42","author":"DT Manallack","year":"2013","unstructured":"Manallack DT, Prankerd RJ, Yuriev E, Oprea TI, Chalmers DK (2013) The significance of acid\/base properties in drug discovery. Chem Soc Rev 42(2):485\u2013496. https:\/\/doi.org\/10.1039\/C2CS35348B","journal-title":"Chem Soc Rev"},{"issue":"23","key":"362_CR2","doi-asserted-by":"publisher","first-page":"9701","DOI":"10.1021\/jm501000a","volume":"57","author":"PS Charifson","year":"2014","unstructured":"Charifson PS, Walters WP (2014) Acidic and basic drugs in medicinal chemistry: a perspective. J Med Chem 57(23):9701\u20139717. https:\/\/doi.org\/10.1021\/jm501000a","journal-title":"J Med Chem"},{"issue":"2","key":"362_CR3","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1002\/cmdc.201200507","volume":"8","author":"DT Manallack","year":"2013","unstructured":"Manallack DT, Prankerd RJ, Nassta GC, Ursu O, Oprea TI, Chalmers DK (2013) A chemogenomic analysis of ionization constants-implications for drug discovery. ChemMedChem 8(2):242\u2013255. https:\/\/doi.org\/10.1002\/cmdc.201200507","journal-title":"ChemMedChem"},{"issue":"1","key":"362_CR4","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1021\/acs.jctc.8b00826","volume":"15","author":"C de Oliveira","year":"2019","unstructured":"de Oliveira C, Yu HS, Chen W, Abel R, Wang L (2019) Rigorous free energy perturbation approach to estimating relative binding affinities between ligands with multiple protonation and tautomeric states. J Chem Theory Comput 15(1):424\u2013435. https:\/\/doi.org\/10.1021\/acs.jctc.8b00826","journal-title":"J Chem Theory Comput"},{"issue":"2","key":"362_CR5","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/0307-4412(94)00150-N","volume":"23","author":"IG Darvey","year":"1995","unstructured":"Darvey IG (1995) The assignment of pKa values to functional groups in amino acids. Biochem Educ 23(2):80\u201382. https:\/\/doi.org\/10.1016\/0307-4412(94)00150-N","journal-title":"Biochem Educ"},{"issue":"3","key":"362_CR6","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1021\/ed063p246","volume":"63","author":"GM Bodner","year":"1986","unstructured":"Bodner GM (1986) Assigning the pKa\u2019s of polyprotic acids. J Chem Educ 63(3):246. https:\/\/doi.org\/10.1021\/ed063p246","journal-title":"J Chem Educ"},{"key":"362_CR7","first-page":"217","volume":"95","author":"R Murray","year":"1995","unstructured":"Murray R (1995) Microscopic equilibria. Anal Chem 95:217","journal-title":"Anal Chem"},{"issue":"10","key":"362_CR8","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s10822-018-0168-0","volume":"32","author":"M I\u015f\u0131k","year":"2018","unstructured":"I\u015f\u0131k M, Levorse D, Rustenburg AS, Ndukwe IE, Wang H, Wang X, Reibarkh M, Martin GE, Makarov AA, Mobley DL, Rhodes T, Chodera JD (2018) pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 32(10):1117\u20131138. https:\/\/doi.org\/10.1007\/s10822-018-0168-0","journal-title":"J Comput Aided Mol Des"},{"issue":"12","key":"362_CR9","doi-asserted-by":"publisher","first-page":"6001","DOI":"10.1021\/acs.jctc.6b00805","volume":"12","author":"AD Bochevarov","year":"2016","unstructured":"Bochevarov AD, Watson MA, Greenwood JR, Philipp DM (2016) Multiconformation, density functional theory-based p $$K_{{\\rm a}}$$ prediction in application to large, flexible organic molecules with diverse functional groups. J Chem Theory Comput 12(12):6001\u20136019. https:\/\/doi.org\/10.1021\/acs.jctc.6b00805","journal-title":"J Chem Theory Comput"},{"issue":"10","key":"362_CR10","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1007\/s10822-018-0138-6","volume":"32","author":"E Selwa","year":"2018","unstructured":"Selwa E, Kenney IM, Beckstein O, Iorga BI (2018) SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies. J Comput Aided Mol Des 32(10):1203\u20131216. https:\/\/doi.org\/10.1007\/s10822-018-0138-6","journal-title":"J Comput Aided Mol Des"},{"issue":"11","key":"362_CR11","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/s10822-016-9955-7","volume":"30","author":"FC Pickard","year":"2016","unstructured":"Pickard FC, K\u00f6nig G, Tofoleanu F, Lee J, Simmonett AC, Shao Y, Ponder JW, Brooks BR (2016) Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. J Comput Aided Mol Des 30(11):1087\u20131100. https:\/\/doi.org\/10.1007\/s10822-016-9955-7","journal-title":"J Comput Aided Mol Des"},{"issue":"10","key":"362_CR12","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1007\/s10822-018-0169-z","volume":"32","author":"CC Bannan","year":"2018","unstructured":"Bannan CC, Mobley DL, Skillman AG (2018) SAMPL6 challenge results from $$pK\\_a$$ predictions based on a general Gaussian process model. J Comput Aided Mol Des 32(10):1165\u20131177. https:\/\/doi.org\/10.1007\/s10822-018-0169-z","journal-title":"J Comput Aided Mol Des"},{"issue":"4","key":"362_CR13","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s10822-019-00271-3","volume":"34","author":"M I\u015f\u0131k","year":"2020","unstructured":"I\u015f\u0131k M, Levorse D, Mobley DL, Rhodes T, Chodera JD (2020) Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des 34(4):405\u2013420. https:\/\/doi.org\/10.1007\/s10822-019-00271-3","journal-title":"J Comput Aided Mol Des"},{"issue":"4","key":"362_CR14","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10822-020-00295-0","volume":"34","author":"M I\u015f\u0131k","year":"2020","unstructured":"I\u015f\u0131k M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL (2020) Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P challenge. J Comput Aided Mol Des 34(4):335\u2013370. https:\/\/doi.org\/10.1007\/s10822-020-00295-0","journal-title":"J Comput Aided Mol Des"},{"issue":"4","key":"362_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.2174\/157016305775202964","volume":"2","author":"T Kogej","year":"2005","unstructured":"Kogej T, Muresan S (2005) Database mining for pKa prediction. Curr Drug Discov Technol 2(4):221\u2013229. https:\/\/doi.org\/10.2174\/157016305775202964","journal-title":"Curr Drug Discov Technol"},{"key":"362_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-5883-8","volume-title":"pKa prediction for organic acids and bases","author":"DD Perrin","year":"1981","unstructured":"Perrin DD, Dempsey B, Serjeant EP (1981) pKa prediction for organic acids and bases, 1st edn. Chapman and Hall, London","edition":"1"},{"key":"362_CR17","volume-title":"Physical organic chemistry","author":"LP Hammett","year":"1940","unstructured":"Hammett LP (1940) Physical organic chemistry. McGraw-Hill, New York"},{"issue":"20","key":"362_CR18","doi-asserted-by":"publisher","first-page":"5343","DOI":"10.1021\/ja01529a025","volume":"81","author":"RW Taft","year":"1959","unstructured":"Taft RW, Lewis IC (1959) Evaluation of resonance effects on reactivity by application of the linear inductive energy relationship V. Concerning a R scale of resonance effects. J Am Chem Soc 81(20):5343\u20135352. https:\/\/doi.org\/10.1021\/ja01529a025","journal-title":"J Am Chem Soc"},{"issue":"3","key":"362_CR19","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1021\/ci020386s","volume":"43","author":"L Xing","year":"2003","unstructured":"Xing L, Glen RC, Clark RD (2003) Predicting p $$K_{{\\rm a}}$$ by molecular tree structured fingerprints and PLS. J Chem Inf Comput Sci 43(3):870\u2013879. https:\/\/doi.org\/10.1021\/ci020386s","journal-title":"J Chem Inf Comput Sci"},{"issue":"6","key":"362_CR20","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.1021\/ci060129d","volume":"46","author":"J Zhang","year":"2006","unstructured":"Zhang J, Klein\u00f6der T, Gasteiger J (2006) Prediction of p $$K_{{\\rm a}}$$ values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors. J Chem Inf Model 46(6):2256\u20132266. https:\/\/doi.org\/10.1021\/ci060129d","journal-title":"J Chem Inf Model"},{"issue":"11","key":"362_CR21","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1002\/cbdv.200900153","volume":"6","author":"G Cruciani","year":"2009","unstructured":"Cruciani G, Milletti F, Storchi L, Sforna G, Goracci L (2009) In silico p $$K_{{\\rm a}}$$ prediction and ADME profiling. Chem Biodiv 6(11):1812\u20131821. https:\/\/doi.org\/10.1002\/cbdv.200900153","journal-title":"Chem Biodiv"},{"issue":"6","key":"362_CR22","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.1021\/ci700018y","volume":"47","author":"F Milletti","year":"2007","unstructured":"Milletti F, Storchi L, Sforna G, Cruciani G (2007) New and original p $$K_{{\\rm a}}$$ prediction method using grid molecular interaction fields. J Chem Inf Model 47(6):2172\u20132181. https:\/\/doi.org\/10.1021\/ci700018y","journal-title":"J Chem Inf Model"},{"key":"362_CR23","volume-title":"Reference module in chemistry, molecular sciences and chemical engineering","author":"R Fraczkiewicz","year":"2013","unstructured":"Fraczkiewicz R (2013) In silico prediction of ionization. In: Hage DS (ed) Reference module in chemistry, molecular sciences and chemical engineering. Elsevier, Amsterdam"},{"key":"362_CR24","unstructured":"Simulations Plus ADMET Predictor v8.5;. Simulations Plus, Lancaster, CA, 2018. https:\/\/www.simulations-plus.com\/software\/admetpredictor\/physicochemical-biopharmaceutical\/"},{"issue":"12","key":"362_CR25","doi-asserted-by":"publisher","first-page":"5933","DOI":"10.1021\/acs.jctc.7b00875","volume":"13","author":"BK Radak","year":"2017","unstructured":"Radak BK, Chipot C, Suh D, Jo S, Jiang W, Phillips JC, Schulten K, Roux B (2017) Constant-pH molecular dynamics simulations for large biomolecular systems. J Chem Theory Comput 13(12):5933\u20135944. https:\/\/doi.org\/10.1021\/acs.jctc.7b00875","journal-title":"J Chem Theory Comput"},{"issue":"5","key":"362_CR26","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s10822-020-00280-7","volume":"34","author":"MR Gunner","year":"2020","unstructured":"Gunner MR, Murakami T, Rustenburg AS, I\u015f\u0131k M, Chodera JD (2020) Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states. J Comput Aided Mol Des 34(5):561\u2013573. https:\/\/doi.org\/10.1007\/s10822-020-00280-7","journal-title":"J Comput Aided Mol Des"},{"issue":"5","key":"362_CR27","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1021\/jp026454v","volume":"107","author":"GM Ullmann","year":"2003","unstructured":"Ullmann GM (2003) Relations between protonation constants and titration curves in polyprotic acids: a critical view. J Phys Chem B 107(5):1263\u20131271. https:\/\/doi.org\/10.1021\/jp026454v","journal-title":"J Phys Chem B"},{"key":"362_CR28","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1002\/prot.340150304","volume":"15","author":"AS Yang","year":"1993","unstructured":"Yang AS, Gunner MR, Sampogna R, Sharp K, Honig B (1993) On the calculation of pKas in proteins. Proteins 15:252\u2013265","journal-title":"Proteins"},{"key":"362_CR29","unstructured":"Special Issue: SAMPL6 (Statistical Assessment of the Modeling of Proteins and Ligands (2018) J Comput Aided Mol Design 32(10)"},{"issue":"12","key":"362_CR30","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 (2007) Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681\u2013691. https:\/\/doi.org\/10.1007\/s10822-007-9133-z","journal-title":"J Comput Aided Mol Des"},{"key":"362_CR31","unstructured":"QUACPAC Toolkit (2017) OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com"},{"key":"362_CR32","unstructured":"OEChem Toolkit (2017) OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com"},{"issue":"1\u20132","key":"362_CR33","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn HW (1955) The Hungarian method for the assignment problem. Naval Res Log Q 2(1\u20132):83\u201397. https:\/\/doi.org\/10.1002\/nav.3800020109","journal-title":"Naval Res Log Q"},{"issue":"1","key":"362_CR34","first-page":"28","volume":"5","author":"J Munkres","year":"1957","unstructured":"Munkres J (1957) Algorithms for the assignment and transportation problems. J SIAM 5(1):28\u201332","journal-title":"J SIAM"},{"key":"362_CR35","unstructured":"SciPy v1.3.1 (2019) Linear Sum Assignment Documentation. The SciPy community. https:\/\/docs.scipy.org\/doc\/scipy-1.3.1\/reference\/generated\/scipy.optimize.linear_sum_assignment.html"},{"key":"362_CR36","unstructured":"OpenEye pKa Prospector;. OpenEye Scientific Software, Santa Fe, NM. https:\/\/www.eyesopen.com\/pka-prospector accessed on Jan 23, 2018"},{"key":"362_CR37","unstructured":"ACD\/pKa GALAS (ACD\/Percepta Kernel v1.6);. Advanced Chemistry Development, Inc., Toronto, ON, Canada, 2018. https:\/\/www.acdlabs.com\/products\/percepta\/predictors\/pKa\/"},{"key":"362_CR38","unstructured":"ACD\/pKa Classic (ACD\/Percepta Kernel v1.6);. Advanced Chemistry Development, Inc., Toronto, ON, Canada, 2018. https:\/\/www.acdlabs.com\/products\/percepta\/predictors\/pKa\/"},{"key":"362_CR39","unstructured":"Chemicalize v18.23 (ChemAxon MarvinSketch v18.23);. ChemAxon, Budapest, Hungary, 2018. https:\/\/docs.chemaxon.com\/display\/docs\/pKa+Plugin"},{"key":"362_CR40","unstructured":"MoKa;. Molecular Discovery, Hertfordshire, UK, 2018. https:\/\/www.moldiscovery.com\/software\/moka\/"},{"issue":"10","key":"362_CR41","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1007\/s10822-018-0150-x","volume":"32","author":"Q Zeng","year":"2018","unstructured":"Zeng Q, Jones MR, Brooks BR (2018) Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge. J Comput Aided Mol Des 32(10):1179\u20131189. https:\/\/doi.org\/10.1007\/s10822-018-0150-x","journal-title":"J Comput Aided Mol Des"},{"issue":"18","key":"362_CR42","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1002\/qua.24481","volume":"113","author":"AD Bochevarov","year":"2013","unstructured":"Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113(18):2110\u20132142. https:\/\/doi.org\/10.1002\/qua.24481","journal-title":"Int J Quantum Chem"},{"issue":"10","key":"362_CR43","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1007\/s10822-018-0140-z","volume":"32","author":"N Tielker","year":"2018","unstructured":"Tielker N, Eberlein L, G\u00fcssregen S, Kast SM (2018) The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory. J Comput Aided Mol Des 32(10):1151\u20131163. https:\/\/doi.org\/10.1007\/s10822-018-0140-z","journal-title":"J Comput Aided Mol Des"},{"issue":"44","key":"362_CR44","doi-asserted-by":"publisher","first-page":"9380","DOI":"10.1021\/jp034688o","volume":"107","author":"A Klamt","year":"2003","unstructured":"Klamt A, Eckert F, Diedenhofen M, Beck ME (2003) First principles calculations of aqueous p $$K_{{\\rm a}}$$ values for organic and inorganic acids using COSMO-RS reveal an inconsistency in the slope of the p $$K_{{\\rm a}}$$ scale. J Phys Chem A 107(44):9380\u20139386. https:\/\/doi.org\/10.1021\/jp034688o","journal-title":"J Phys Chem A"},{"issue":"1","key":"362_CR45","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/jcc.20309","volume":"27","author":"F Eckert","year":"2006","unstructured":"Eckert F, Klamt A (2006) Accurate prediction of basicity in aqueous solution with COSMO-RS. J Comput Chem 27(1):11\u201319. https:\/\/doi.org\/10.1002\/jcc.20309","journal-title":"J Comput Chem"},{"issue":"10","key":"362_CR46","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1007\/s10822-018-0145-7","volume":"32","author":"P Pracht","year":"2018","unstructured":"Pracht P, Wilcken R, Udvarhelyi A, Rodde S, Grimme S (2018) High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge. J Comput Aided Mol Des 32(10):1139\u20131149. https:\/\/doi.org\/10.1007\/s10822-018-0145-7","journal-title":"J Comput Aided Mol Des"},{"issue":"10","key":"362_CR47","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1007\/s10822-018-0167-1","volume":"32","author":"S Prasad","year":"2018","unstructured":"Prasad S, Huang J, Zeng Q, Brooks BR (2018) An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge. J Comput Aided Mol Des 32(10):1191\u20131201. https:\/\/doi.org\/10.1007\/s10822-018-0167-1","journal-title":"J Comput Aided Mol Des"},{"key":"362_CR48","unstructured":"Robert\u00a0Fraczkiewicz MW (2018) SAMPL6 pKa Challenge: Predictions of ionization constants performed by the S+pKa method implemented in ADMET Predictor software. The Joint D3R\/SAMPL Workshop 2018. https:\/\/drugdesigndata.org\/about\/d3r-2018-workshop"},{"key":"362_CR49","unstructured":"OEMolProp Toolkit 2017.Feb.1;. OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com"},{"key":"362_CR50","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jpba.2012.04.021","volume":"67\u201368","author":"GT Balogh","year":"2012","unstructured":"Balogh GT, Tarcsay \u00c1, Keser\u0171 GM (2012) Comparative evaluation of pKa prediction tools on a drug discovery dataset. J Pharm Biomed Anal 67\u201368:63\u201370. https:\/\/doi.org\/10.1016\/j.jpba.2012.04.021","journal-title":"J Pharm Biomed Anal"},{"issue":"4","key":"362_CR51","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1007\/s11095-013-1232-z","volume":"31","author":"L Settimo","year":"2014","unstructured":"Settimo L, Bellman K, Knegtel RMA (2014) Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm Res 31(4):1082\u20131095. https:\/\/doi.org\/10.1007\/s11095-013-1232-z","journal-title":"Pharm Res"},{"issue":"4","key":"362_CR52","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1007\/s00216-007-1502-x","volume":"389","author":"M Meloun","year":"2007","unstructured":"Meloun M, Bordovsk\u00e1 S (2007) Benchmarking and Validating algorithms that estimate pK a values of drugs based on their molecular structures. Anal Bioanal Chem 389(4):1267\u20131281. https:\/\/doi.org\/10.1007\/s00216-007-1502-x","journal-title":"Anal Bioanal Chem"},{"issue":"12","key":"362_CR53","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.1021\/ci900289x","volume":"49","author":"C Liao","year":"2009","unstructured":"Liao C, Nicklaus MC (2009) Comparison of nine programs predicting p $$K_{{\\rm a}}$$ values of pharmaceutical substances. J Chem Inf Model 49(12):2801\u20132812. https:\/\/doi.org\/10.1021\/ci900289x","journal-title":"J Chem Inf Model"},{"issue":"4","key":"362_CR54","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1021\/ci100019p","volume":"50","author":"J Manchester","year":"2010","unstructured":"Manchester J, Walkup G, Rivin O, You Z (2010) Evaluation of p $$K_{{\\rm a}}$$ estimation methods on 211 druglike compounds. J Chem Inf Model 50(4):565\u2013571. https:\/\/doi.org\/10.1021\/ci100019p","journal-title":"J Chem Inf Model"},{"key":"362_CR55","first-page":"11","volume":"1","author":"K Mansouri","year":"2019","unstructured":"Mansouri K, Cariello NF, Korotcov A, Tkachenko V, Grulke CM, Sprankle CS, Allen D, Casey WM, Kleinstreuer NC, Williams AJ (2019) Open-source QSAR models for pKa prediction using multiple machine learning approaches. J Cheminf 1:11","journal-title":"J Cheminf"},{"key":"362_CR56","doi-asserted-by":"publisher","first-page":"113","DOI":"10.12688\/f1000research.22090.2","volume":"9","author":"M Baltruschat","year":"2020","unstructured":"Baltruschat M (2020) Machine learning meets pKa [version 2; peer review: 2 approved]. F1000Research 9:113. https:\/\/doi.org\/10.12688\/f1000research.22090.2","journal-title":"F1000Research"},{"issue":"6","key":"362_CR57","doi-asserted-by":"publisher","first-page":"2989","DOI":"10.1021\/acs.jcim.0c00105","volume":"60","author":"P Hunt","year":"2020","unstructured":"Hunt P, Hosseini-Gerami L, Chrien T, Plante J, Ponting DJ, Segall M (2020) Predicting p $$K_{{\\rm a}}$$ using a combination of semi-empirical quantum mechanics and radial basis function methods. J Chem Inf Model 60(6):2989\u20132997. https:\/\/doi.org\/10.1021\/acs.jcim.0c00105","journal-title":"J Chem Inf Model"},{"issue":"11","key":"362_CR58","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.1021\/acs.jmedchem.7b00954","volume":"61","author":"B Zdrazil","year":"2018","unstructured":"Zdrazil B, Guha R (2018) The rise and fall of a scaffold: a trend analysis of scaffolds in the medicinal chemistry literature. J Med Chem 61(11):4688\u20134703. https:\/\/doi.org\/10.1021\/acs.jmedchem.7b00954","journal-title":"J Med Chem"},{"issue":"15","key":"362_CR59","doi-asserted-by":"publisher","first-page":"8408","DOI":"10.1021\/acs.jmedchem.0c00754","volume":"63","author":"P Ertl","year":"2020","unstructured":"Ertl P, Altmann E, McKenna JM (2020) The most common functional groups in bioactive molecules and how their popularity has evolved over time. J Med Chem 63(15):8408\u20138418. https:\/\/doi.org\/10.1021\/acs.jmedchem.0c00754","journal-title":"J Med Chem"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-020-00362-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10822-020-00362-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-020-00362-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T02:28:57Z","timestamp":1614133737000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10822-020-00362-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["362"],"URL":"https:\/\/doi.org\/10.1007\/s10822-020-00362-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.10.15.341792","asserted-by":"object"}]},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"16 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"JDC was a member of the Scientific Advisory Board for Schr\u00f6dinger, LLC during part of this study, and is a current Scientific Advisory Board member for OpenEye Scientific and scientific advisor to Foresite Labs. DLM is a current member of the Scientific Advisory Board of OpenEye Scientific and an Open Science Fellow with Silicon Therapeutics. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Vir Biotechnology, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, The Einstein Foundation, and the Sloan Kettering Institute. A complete list of funding can be found at\n                      \n                      .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}