title: pKa prediction of monoprotic small molecules the SMARTS way.
abstract: Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.
authors.affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Adam C
authors.initials: AC
authors.lastname: Lee
authors.forename: Jing-Yu
authors.initials: JY
authors.lastname: Yu
authors.forename: Gordon M
authors.initials: GM
authors.lastname: Crippen
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D004353:Drug Evaluation, Preclinical; D005544:Forecasting; D007700:Kinetics; D008958:Models, Molecular; D010449:Peptide Mapping; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D054852:Small Molecule Libraries; D012984:Software; D013329:Structure-Activity Relationship; D013358:Subject Headings
chemical_list: D004364:Pharmaceutical Preparations; D054852:Small Molecule Libraries