ANI neural network potentials for small molecule pKa prediction
Urquhart, Ross James and van Teijlingen, Alexander and Tuttle, Tell (2024) ANI neural network potentials for small molecule pKa prediction. Physical Chemistry Chemical Physics, 26 (36). pp. 23934-23943. ISSN 1463-9084 (https://doi.org/10.1039/D4CP01982B)
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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.
ORCID iDs
Urquhart, Ross James ORCID: https://orcid.org/0000-0001-8505-2798, van Teijlingen, Alexander ORCID: https://orcid.org/0000-0002-3739-8943 and Tuttle, Tell;-
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Item type: Article ID code: 90487 Dates: DateEvent29 August 2024Published29 August 2024Published Online28 August 2024AcceptedSubjects: Science > Chemistry > Physical and theoretical chemistry Department: Faculty of Science > Pure and Applied Chemistry
Technology and Innovation Centre > BionanotechnologyDepositing user: Pure Administrator Date deposited: 05 Sep 2024 13:30 Last modified: 01 Oct 2024 08:21 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90487