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Predicting pKa Using a Combination of Quantum Mechanical and Machine Learning Methods

Monday, 18 November 2019 13:23
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Peter Hunt

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 (pKa) 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 pKa 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 pKa 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.

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Last Updated on Monday, 18 November 2019 13:53  
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