Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions

Early
assessment of crystalline thermodynamic solubility continues
to be elusive for drug discovery and development despite its critical
importance, especially for the ever-increasing fraction of poorly
soluble drug candidates. Here we present a detailed evaluation of
a physics-based free energy perturbation (FEP+) approach for computing
the thermodynamic aqueous solubility. The predictive power of this
approach is assessed across diverse chemical spaces, spanning pharmaceutically
relevant literature compounds and more complex AbbVie compounds. Our
approach achieves predictive (RMSE = 0.86) and differentiating power
(R2 = 0.69) and therefore provides notably
improved correlations to experimental solubility compared to state-of-the-art
machine learning approaches that utilize quantum mechanics-based descriptors.
The importance of explicit considerations of crystalline packing in
predicting solubility by the FEP+ approach is also highlighted in
this study. Finally, we show how computed energetics, including hydration
and sublimation free energies, can provide further insights into molecule
design to feed the medicinal chemistry DMTA cycle.