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Open Access COVID-19 Drug Discovery Project Launched

March 2021

Accelerating Drug Discovery

With the ongoing COVID-19 global pandemic at the forefront of our minds, we are launching the Aionics Open Access Drug Discovery Project: a fully free and open access version of the Aionics molecular modeling suite, focused solely on COVID-19 drug discovery. By leveraging our platform and contributions from the broader research community, this project aims to collect and cross-reference COVID-19 research datasets, build models from them, and use the models to down-select promising candidates from millions of commercially available substances – all in the public domain.

Reports on the efficacy of new drugs for treating COVID-19 are surfacing regularly, with notable recent examples including the news of Northwell Health’s clinical trials of famotidine, results from Gilead Science’s clinical trials of remdesivir, and the first (non-peer reviewed) report of randomized clinical trials of hydroxychloroquine. However, it is difficult to put these emerging results into the broader context of ongoing research efforts, as each new result changes the collective understanding of possible approaches to treating COVID-19. The Open Access COVID-19 Drug Discovery Project provides a centralized platform for tracking research progress, updating drug efficacy models, and highlighting the changing lists of the most promising candidate drugs in real-time as results are published. With initial studies already emerging, the urgency for deploying this proposed platform is high. The goal is to build a centralized system to facilitate knowledge sharing and coordination across global research efforts, which are highly decentralized by nature.

ML-based drug discovery is a well-established field dating back over 20 years but has experienced a significant acceleration in the last five years. A number of success stories exist including the identification of targets for Huntington’s disease, identification of therapeutic resistance from disease-specific splice variants, and prediction of cancer drug response. It is unknown a priori whether machine learning (ML)-based modeling will successfully identify promising antiviral compounds for COVID-19, but the impact of such a discovery would clearly be significant. This project represents a systematic application of knowledge towards the production of useful antiviral drugs by leveraging literature data and state-of-the-art ML techniques to enable rapid identification of promising candidates for further study. By making the results and pipeline free and open to the public, we aim for this project to accelerate drug discovery efforts across the broader research community.

We believe the opportunity has never been greater for an open access drug discovery platform to provide real worldwide value to ongoing research efforts. Through the Open Access COVID-19 Drug Discovery Project, we are excited to provide such a tool to the community, and hopeful to see it be used by researchers we have never met, to generate impact in ways we never imagined.

To access the platform, visit:

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