Predicting Drug Solubility Using Different Machine Learning Methods - Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network

Author(s): John Ho, Zhaoheng Yin, Colin Zhang, Nicole Guo, Yuwei Xia, Yang Ha.

Predicting the solubility of given molecules remains crucial in the pharmaceutical industry. In this study, we revisited this extensively studied topic, leveraging the capabilities of contemporary computing resources by employing two machine learning models: a linear regression model and a graph convolutional neural network (GCNN) model. Using various experimental datasets, both methods yielded reasonable predictions. Despite its highest level of performance, the GCNN model has limited interpretability. On the other hand, although more human inputs and evaluations on the overall dataset is required, the linear regression model allows scientists for a greater in-depth analysis of the underlying factors through feature importance analysis. From the chemistry perspective, using the linear regression model elucidates the impact of individual atom species and functional groups on overall solubility, highlighting the significance of comprehending how chemical structure influences chemical properties in the drug development process. It has been learned that introducing oxygen atoms can increase the solubility of organic molecules, while almost all other hetero atoms except oxygen and nitrogen tend to decrease solubility.