Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

Floriane Montanari, Lara Kuhnke, Antonius Ter Laak, Djork-Arné Clevert
2019 Molecules  
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling all the Bayer in-house data related to these properties allowed us to apply powerful machine learning techniques to predict the outcome of those assays for new compounds. In this paper, we report our
more » ... nding that, especially for predicting physicochemical ADMET endpoints, a multitask graph convolutional approach appears a highly competitive choice. For seven endpoints of interest, we compared the performance of that approach to fully connected neural networks and different single task models. The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized. In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint.
doi:10.3390/molecules25010044 pmid:31877719 pmcid:PMC6982787 fatcat:4jfr73hkd5hvhbzilp56vkmb64