KNIME workflows for applications in medicinal and computational chemistry

Abstract

Artificial intelligence (AI) has huge potential to accelerate drug discovery, but challenges remain in implementing AI algorithms that can be used by the broad scientific community. Identification of molecular features and their subsequent use in training of machine learning models may permit prediction of new molecules with enhanced properties. Predictive modeling is particularly applicable to analysis of structure-activity relationships (SARs) and would be a useful tool in the hands of laboratory medicinal chemists. This requires a software platform that is chemically intuitive while providing the user with access to AI methods. The KNIME platform provides such an environment through inclusion of broad chemical toolsets and a user-friendly approach for utilization of machine learning for analysis of SAR data. Here, we illustrate use of KNIME for this purpose, with a focus on discovery of features of highly potent tau inhibitors from a series of structurally diverse polyphenols. Workflows are described that enable implementation of AI tools in KNIME for diverse SAR projects.