In silico pKa Prediction and ADME Profiling

Abstract

Improving the ADME profile of drug candidates is a critical step in lead optimization, and because pK_a affects most ADME properties such as lipophilicity, solubility, and metabolism, it is extremely advantageous to predict pK_a in order to guide the design of new compounds. However, accurately (K_a by empirical methods can be challenging especially for novel series, because of lack of knowledge on determinants of pK_a (steric effects, ring effects, H-bonding, etc.), and because of limited experimental data on the effects of specific chemical groups on the ionization of an atom. To address these issues, we recently developed the computational package MoKa, which integrates graphical and command line tools designed for computational and medicinal chemists to predict the pK_a values of organic compounds. Here, we present the major issues considered when we developed MoKa, such as the accurate selection of training data, the fundamentals of the methodology (which has also been extended to predict protein pK_a), the treatment of multiprotic compounds, and the selection of the dominant tautomer for the calculation. Last, we illustrate some specific applications of MoKa to predict solubility, lipophilicity, and metabolism.