Development and sharing of ADME/Tox and drug discovery machine learning model

On the order of hundreds of ADME/Tox models have been described in the literature in the last decade which are more often than not inaccessible to anyone but the authors. Public accessibility is also an issue with computational models for bioactivity. A major challenge limiting drug discovery still remains the ability to share such models. We now describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Toolkit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build 18 Bayesian models for ADME/Tox, in vitro and in vivo bioactivity and other physicochemical properties. We show that these models possess cross validation ROC values comparable to those generated previously in prior publications using alternative tools. We have now described how implementation of Bayesian models with FCFP6 descriptors generated in the CDD vault enables the rapid production of robust machine learning models from public data, or the user’s own datasets. The current study sets the stage for generating models in proprietary software such as CDD and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.