What’s happening at Polaris.

Due to the scattered and inaccessible nature of evaluative insights, innovation in machine learning for drug discovery often struggle to translate into real drug discovery programs. At Polaris, we are actively re-thinking how ML methods are assessed and compared in order to bridge this gap. Let’s take a closer look at what this future of ML evaluation in drug discovery could look like!

Competitions are a critical tool to evaluate the current state of the art in computational molecular sciences. As par of its open science mission, the ASAP Discovery Consortium is launching an antiviral competition in collaboration with OpenADMET. Test your skills in ligand pose prediction, potency prediction, and ADMET prediction, all revolving around SARS-CoV-2 and MERS-CoV Mpro.

While fields like natural language processing (NLP) and computer vision (CV) have long benefitted from standardized benchmarks and well-organized competitions, ML for drug discovery (MLDD) faces unique challenges. What can we learn from other fields and what does the current landscape of competitions look like in MLDD?