In the lead optimization stage of drug discovery, optimizing solubility of lead drug-like molecules is an essential step to make them orally bioavailable. However, improving or maintaining solubility by trial and error, iteratively making compounds and measuring their solubility experimentally, is costly and may not succeed as well within project timescales. Computational prediction of solubility is often empirical, via fitting to datasets of experimentally measured solubility using different regression models, but this tends to suggest simply increasing polarity of a compound to improve its solubility. Here we present a novel physics-based approach to predict the intrinsic solubility of small molecules by taking into account three-dimensional solid-state characteristics in addition to polarity. The aqueous solubility of a drug-like compound is estimated by calculating the sublimation free energies of the compound in its amorphous solid state and its hydration free energy utilizing a thermodynamic cycle. Application of the method to a live pharmaceutical project indicates that our prediction is not only superior than the widely utilized polarity approach but also able to distinguish low soluble compounds, guiding the synthesis decision to focus on compounds with higher solubility.

Computational prediction of the intrinsic solubilities of drug-like molecules utilizing free energy perturbations

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.