Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions

Mehtap Işık, Ariën S Rustenburg, Andrea Rizzi, Marilyn R Gunner, David L Mobley, John D Chodera
Journal of Computer-Aided Molecular Design 35:131, 2021
[DOI] [bioRxiv] [GitHub] [manuscript and figure sources]

The SAMPL6 pKa challenge assessed the ability of the computational chemistry community to predict macroscopic and microscopic pKas for a set of druglike molecules resembling kinase inhibitors. This paper reports on the overall performance and lessons learned, including the surprising finding that many tools predict reasonably accurate macroscopic pKas corresponding to the wrong microscopic protonation sites.

Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states

M R Gunner, Taichi Murakami, Ariën S. Rustenburg, Mehtap Işık, and John D. Chodera.
Journal of Computer Aided Molecular Design 34:561, 2020. [DOI] [PDF] [GitHub]

Here, we demonstrate how the physical nature of protonation and tautomeric state effects means that the standard state free energies of each microscopic protonation/tautomeric state at a single pH is sufficient to describe the complete pH-dependent microscopic and macroscopic populations. We introduce a new kind of diagram that uses this concept to illustrate a variety of pH-dependent phenomena, and show how it can be used to identify common issues with protonation state prediction algorithms. As a result, we recommend future blind prediction challenges utilize microstate free energies at a single reference pH as the minimal sufficient information for assessing prediction accuracy and utility.

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments

Mehtap Işık, Dorothy Levorse, Ariën S. Rustenburg, Ikenna E. Ndukwe, Heather Wang , Xiao Wang , Mikhail Reibarkh , Gary E. Martin , Alexey A. Makarov , David L. Mobley, Timothy Rhodes*, John D. Chodera*.
* co-corresponding authors
Journal of Computer-Aided Molecular Design special issue on SAMPL6 32:1117, 2018.
[DOI] [PDF] [bioRxiv] [Supplementary Tables and Figures] [Supplementary Data (includes Sirius T3 reports on all measurements)]

The SAMPL5 blind challenge exercises identified neglect of protonation state effects as a major accuracy-limiting factor in physical modeling of biomolecular interactions. In this study, we report the experimental measurements behind a SAMPL6 blind challenges in which we assess the ability of community codes to predict small molecule pKas for small molecule resembling fragments of selective kinase inhibitors.