Solvate Prediction for Pharmaceutical Organic Molecules with Machine Learning

作者：Dongyue Xin, Nina C. Gonnella, Xiaorong He, Keith R. Horspool · 发表于：Crystal Growth & Design · 年份：2019 · DOI：10.1021/acs.cgd.8b01883 · 被引用次数：57 · 研究领域：Computational Drug Discovery Methods、Crystallization and Solubility Studies、Machine Learning in Materials Science

Methods to predict crystallization behavior for active pharmaceutical ingredients (APIs) can serve as an important guide in small molecule pharmaceutical development. Here, we describe solvate formation propensity prediction for pharmaceutical molecules via a machine learning approach. Random forests (RF) and support vector machine (SVM) algorithms were trained and tested with data sets extracted from Cambridge Structural Database (CSD). The machine learning models, requiring only 2D structures as input, were able to predict solvate formation propensity for organic molecules with up to 86% success rate. Performance of the models was demonstrated with a collection of 20 pharmaceutical molecules.