Held on 13 November 2019.

In this webinar, presented by Matt Segall, we introduce the exciting new features in StarDrop 6.6, including: A new pKa model, included in the ADME QSAR module, combines quantum mechanics and machine learning to accurately predict acid dissociation constants for your compounds. An extended suite of SeeSAR modules that enhance StarDrop’s capabilities in structure-based design.

_{To hear the narration, please increase your speaker volume.}

Preprint Paper.

Peter Hunt¹, Layla Hosseini-Gerami², Tomas Chrien¹, Jeffrey Plante³, David J. Ponting³, Matthew Segall¹

¹Optibrium Ltd. ²Department of Chemistry, Cambridge. ³Lhasa Ltd

Abstract

The acid dissociation constant (pK_a) has an important influence on molecular properties crucial to compound development in synthesis, formulation and optimisation of absorption, distribution, metabolism and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pK_a for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pK_a prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7 – 1.0 log units), comparable to other methodologies using much higher level of theory and computational cost.

You can download the paper as a PDF