Multi-Solvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors
| Autor: | Antonio Llinas, Vigneshwari Subramanian, Maxim V. Fedorov, Ekaterina L. Ratkova, David S. Palmer, Ola Engkvist |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
010304 chemical physics Chemistry General Chemical Engineering Organic solvent Entropy Solvation Thermodynamics Water General Chemistry Library and Information Sciences 01 natural sciences 0104 chemical sciences Computer Science Applications Root mean square Solvent Solutions 010404 medicinal & biomolecular chemistry Solvent models 0103 physical sciences Solvents QD Free energies Energy (signal processing) |
| ISSN: | 1549-9596 |
| Popis: | The potential to predict Solvation Free Energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted 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. |
| Databáze: | OpenAIRE |
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