Multiconformation, Density Functional Theory-Based pKa Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups

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• 作者：Art D. Bochevarov ; Mark A. Watson ; Jeremy R. Greenwood ; Dean M. Philipp
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2016
• 出版时间：December 13, 2016
• 年：2016
• 卷：12
• 期：12
• 页码：6001-6019
• 全文大小：781K
• ISSN：1549-9626

文摘

We consider the conformational flexibility of molecules and its implications for micro- and macro-pK_a. The corresponding formulas are derived and discussed against the background of a comprehensive scientific and algorithmic description of the latest version of our computer program Jaguar pK_a, a density functional theory-based pK_a predictor, which is now capable of acting on multiple conformations explicitly. Jaguar pK_a is essentially a complex computational workflow incorporating research and technologies from the fields of cheminformatics, molecular mechanics, quantum mechanics, and implicit solvation models. The workflow also makes use of automatically applied empirical corrections which account for the systematic errors resulting from the neglect of explicit solvent interactions in the algorithm’s implicit solvent model. Applications of our program to large, flexible organic molecules representing several classes of functional groups are shown, with a particular emphasis in illustrations laid on drug-like molecules. It is demonstrated that a combination of aggressive conformational search and an explicit consideration of multiple conformations nearly eliminates the dependence of results on the initially chosen conformation. In certain cases this leads to unprecedented accuracy, which is sufficient for distinguishing stereoisomers that have slightly different pK_a values. An application of Jaguar pK_a to proton sponges, the pK_a of which are strongly influenced by steric effects, showcases the advantages that pK_a predictors based on quantum mechanical calculations have over similar empirical programs.