支持向量机
随机森林
结晶
药学
有机分子
人工智能
制药工业
活性成分
分子
计算机科学
化学
机器学习
有机化学
生物信息学
病理
药理学
生物
医学
作者
Dongyue Xin,Nina C. Gonnella,Xiaorong He,Keith R. Horspool
标识
DOI:10.1021/acs.cgd.8b01883
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI