Explore novel drug candidates for COVID-19

The traditional drug discovery pipeline is time and cost intensive. It can take up to 10 years and cost as much as $2.6 billion for a new drug to reach market. To deal with new viral outbreaks and epidemics, such as COVID-19, we need more rapid drug discovery processes. Generative AI models have shown promise for automating the discovery of molecules. However, there are many challenges in applying existing generative AI frameworks to accelerate the design of novel drug candidates. Current generative frameworks are not efficient in handling design tasks with multiple discovery constraints, have limited exploratory and expansion capabilities, and require expensive model retraining to learn beyond limited training data, both in terms of targets and ligands.

For the last two years, we have been developing robust generative frameworks that can overcome these challenges to create novel peptides, proteins, drug candidates, and materials. We have applied our methodology to generate drug-like molecule candidates for novel COVID-19 targets. Our hope is that by releasing these novel molecules, the research and drug design communities can accelerate the process of identifying promising new drug candidates for coronavirus and potential similar, new outbreaks.

We also hope this work will demonstrate our vision for the future of accelerated discovery, where AI researchers and pharmaceutical scientists work together to rapidly create next-generation therapeutics, aided by novel AI-powered tools.

If you are interested in adding or exploring other molecular features, or target proteins, or would like to work with us on further validating the generated molecules, please contact us via email or join the cogmol slack community to make suggestions and contributions, and to ask questions.