Fast and general method to predict the physicochemical properties of druglike molecules using the integral equation theory of molecular liquids

David S. Palmer, Maksim Misin, Maxim V. Fedorov, Antonio Llinas

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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 languageEnglish
Pages (from-to)3420–3432
Number of pages13
JournalMolecular Pharmaceutics
Issue number9
Early online date26 Jul 2015
Publication statusPublished - 8 Sep 2015


  • ADME
  • 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



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