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

药物的溶解度决定了它们被吸收的程度。机器学习算法可以预测新药的溶解度，而无需实际合成它们-从而节省大量时间和金钱。然而，我们目前从真空中单分子的结构推断溶解度-这是一种忽略原子间相互作用的次优方法。该项目由阿斯利康支持，将通过生成晶体药物多晶型物的三维分子模型并通过增强的采样模拟来模拟其溶解来解决这一问题。这些结果将用于构建一个机器学习框架，该框架将揭示药物溶解度的原子起源。

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.