Solvate Prediction for Pharmaceutical Organic Molecules with Machine Learning

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.