Drug-like molecules normally possess multiple protonation / deprotonation sites and are sometimes prone to tautomerism, in addition to having considerable conformational flexibility. These complications render computational pKa predictions for such molecules difficult and error-prone. Previously we have reported a computational workflow that predicts macro-pKa on the basis of density functional theory (DFT) calculations. Due to the inherent complexity of the workflow, it was mainly applicable to small heterocycles and considerably truncated drug-like molecules. The workflow combines innovations in areas of cheminformatics, molecular mehanics, and quantum chemistry to generate conformers and tautomers, compute their micro-pKa's, and combine the results into a macro-pKa that can be directly compared to experimental results. Now we extend the target of our macro-pKa prediction workfow to realistic drug-like molecules. The raised bar associated with larger molecular systems and a greater number of protonation / deprotonation sites they have brings about new challenges. Among the challanges are the need to treat intramolecular hydrogen bonds with molecular dynamics similations which involve implicit water molecules, the management of tautomer candidates to keep their number and quality under control, and a more precise engineering for a balanced allocation of CPU resources. The new workflow has been validated on numerous actual drug molecules and drug candidates, and has been used in prospective pKa predictions in actual drug discovery projects. The complete automation and the accuracy of the macro-pKa workflow make it an attractive solution for physics-based pKa calculations in real-world applications.

Macro-pKa predictions for drug-like molecules

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.