## Abstract

We report a method to predict physicochemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D reference interaction site model of the integral equation theory of molecular liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark data sets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.

Original language | English |
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Pages (from-to) | 3420-3432 |

Number of pages | 13 |

Journal | Molecular Pharmaceutics |

Volume | 12 |

Issue number | 9 |

DOIs | |

Publication status | Published - 8 Sep 2015 |

Externally published | Yes |

## Keywords

- ADME
- ADMET
- bioavailability
- caco-2
- drug discovery
- druglike
- hydration free energy
- IET
- integral equation theory of molecular liquids
- permeability
- QSAR
- QSPR
- Random Forest
- reference interaction site model
- RISM
- solvation free energy
- statistical mechanics