Popularizing best practices through the Polaris benchmarking platform

Machine learning (ML) is driving innovations in drug discovery, but we need to be mindful of the circumstances that set this application apart. The absence of standardized, domain-appropriate datasets, guidelines and tools for the evaluation and comparison of methods has led to a growing gap between perceived progress and real-world impact, delaying the adoption of ML in drug discovery. To bridge this gap, we formed Polaris: An open-science, cross-industry and interdisciplinary collaboration to develop benchmarking protocols tailored to drug discovery. With Polaris, we go beyond publishing papers and develop a multimodal benchmarking platform for machine learning in drug discovery to simplify the adoption of these protocols. This talk will share some of the guiding design principles we followed when building Polaris and discuss the role of open-source software in popularizing appropriate benchmarking protocols.