Multi-solvent models for solvation free energy predictions using 3D-RISM hydration thermodynamic descriptors
Subramanian, Vigneshwari and Ratkova, Ekaterina and Palmer, David S. and Engkvist, Ola and Fedorov, Maxim V. and Llinas, Antonio (2020) Multi-solvent models for solvation free energy predictions using 3D-RISM hydration thermodynamic descriptors. Journal of Chemical Information and Modeling, 60 (6). pp. 2977-2988. ISSN 1549-9596 (https://doi.org/10.1021/acs.jcim.0c00065)
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Abstract
The potential to predict Solvation Free Energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model’s capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multi-solvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space.
ORCID iDs
Subramanian, Vigneshwari, Ratkova, Ekaterina, Palmer, David S. ORCID: https://orcid.org/0000-0003-4356-9144, Engkvist, Ola, Fedorov, Maxim V. and Llinas, Antonio;-
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Item type: Article ID code: 72126 Dates: DateEvent22 June 2020Published21 April 2020Published Online13 April 2020AcceptedSubjects: Science > Chemistry Department: Faculty of Science > Pure and Applied Chemistry
Technology and Innovation Centre > Bionanotechnology
Faculty of Science > PhysicsDepositing user: Pure Administrator Date deposited: 21 Apr 2020 15:43 Last modified: 17 Nov 2024 06:46 URI: https://strathprints.strath.ac.uk/id/eprint/72126