Work

ANI neural network potentials for small molecule pKa prediction

Year: 2024

Type: article

Abstract: The pKa value of a molecule is of interest to chemists across a broad spectrum of fields including pharmacology, environmental chemistry and theoretical chemistry. Determination of pKa values can be accomplished through several experimental methods such as NMR techniques and titration together with computational techniques such as DFT calculations. However, all of these methods remain time consuming and computationally expensive. In this work we develop a method for the rapid calculation of pKa values of small molecules which utilises a combination of neural network potentials, low energy conformer searches and thermodynamic cycles. We show that neural network potentials trained on different phase and charge states can be employed in tandem to predict the full thermodynamic energy cycle of molecules. Focusing here on imidazolium derived carbene species, the method utilised can easily be extended to other functional groups of interest such as amines with further training.

Source: Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)

Authors: Urquhart, Ross James, van Teijlingen, Alexander, Tuttle, Tell

Institution: University of Strathclyde

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: G3270299662

OpenAlex ID: W7007909304