Modeling Physico-Chemical ADMET Endpoints With Multitask Graph Convolutional Networks

Simple physico-chemical properties like logD, solubility or serum albumin binding have a direct impact on the likelihood of success of compounds in clinical trials. Here, we collected all the Bayer in house data related to these properties and applied machine learning techniques to predict them for new compounds. We report that, for the endpoints studied here, a multitask graph convolutional network appears a highly competitive choice. The new model shows increased predictive performance on all endpoints compared to previous modeling methods.