Towards physics-based solubility computation for pharmaceuticals to rival informatics

Daniel J. Fowles, David S. Palmer, Rui Guo, Sarah L. Price, John B. O. Mitchell

Research output: Contribution to journalArticlepeer-review


We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work which relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential based Ψmol method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either Molecular Dynamics Free Energy Perturbation simulations or Density Functional Theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid and the pharmaceutical desloratidine show how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods.
Original languageEnglish
JournalJournal of Chemical Theory and Computation
Publication statusAccepted/In press - 30 Apr 2021


  • intrinsic aqueous solubility
  • sublimation
  • hydration
  • free energy
  • lattice
  • polarizable continuum
  • machine learning

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