Projects per year

### Abstract

We report a method to predict physico-chemical 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 datasets. 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 |

Early online date | 26 Jul 2015 |

DOIs | |

Publication status | Published - 8 Sep 2015 |

### 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

## Projects

- 1 Active

## Accurate permeability prediction for “New Modalities” compounds using 3D RISM

1/10/18 → 30/09/21

Project: Research

## Equipment

## Cite this

Palmer, D. S., Misin, M., Fedorov, M. V., & Llinas, A. (2015). Fast and general method to predict the physicochemical properties of druglike molecules using the integral equation theory of molecular liquids.

*Molecular Pharmaceutics*,*12*(9), 3420–3432. https://doi.org/10.1021/acs.molpharmaceut.5b00441