Multi-solvent models for solvation free energy predictions using 3D-RISM hydration thermodynamic descriptors

Vigneshwari Subramanian, Ekaterina Ratkova, David S. Palmer, Ola Engkvist, Maxim V. Fedorov, Antonio Llinas

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)2977-2988
Number of pages12
JournalJournal of Chemical Information and Modeling
Volume60
Issue number6
Early online date21 Apr 2020
DOIs
Publication statusPublished - 22 Jun 2020

Keywords

  • multi solvent models
  • drug metabolism
  • pharmacokinetics

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