It's all in the Structure: Transforming drug design by bringing together molecular simulations and machine learning

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The solubility of pharmaceutical drugs determines to what extent they can be absorbed. Machine learning algorithms can predict the solubility of novel drugs without the need of actually synthetizing them - thus saving substantial time and money. However, we currently infer solubility from the structure of single molecules in vacuum - a sub-optimal approach ignoring interatomic interactions. This project, supported by AstraZeneca, will address this pitfall by generating three-dimensional molecular models of crystalline drugs polymorphs and simulate their dissolution by means of enhanced sampling simulations. These results will be used to construct a machine learning framework that will unravel the atomistic origins of drugs solubility.