One billion molecules against Covid-19

Big problem to solve:

It is critical to rapidly identify molecules/peptides/proteins that can block the development and/or spreading of the 2019 Novel Coronavirus (SARS-CoV-2) and stop Covid-19 related disease. Computational methods (in silico) can quickly screen interactions of existing compounds with Covid-19 targets, but novel methods are needed to get high success rates in the (pre-)clinical phases. In addition, experimental validation of lead compounds is needed to benchmark the in silico hits.

The GrandChallenge: screen a billion possible molecular compounds that can block Covid-19

Improve (in silico or others) methods to identify compounds with blocking interactions relevant to any SARS-CoV-2 target, by optimizing/accelerating the use of HPC (High Performance Computing), Artificial Intelligence, and provide experimental validation.

The GrandChallenge will proceed in three stages, where stage 1 and 2 are sequential and stage 3 is independent. Teams can participate in only one or in multiple stages. The aim is to get high quality lead compounds for multiple SARS-CoV-2 targets by cross-checking multiple simulation approaches. The top scoring compounds will be analyzed experimentally using direct affinity assays, as well viral assays on live SARS-CoV-2. A special prize will be awarded for reduction of the yield of infectious virus by > 99% in cell culture using novel cocktails of already FDA-approved drugs/compounds at sub-micromolar concentrations, and proven effectiveness in an animal model at physiologically relevant doses. All findings will be quickly and publicly shared with the world to battle Covid-19. This challenge is open to any team in the world, requiring breakthrough approaches in computational method and rapid experimental screening. Collaboration of teams in computation, drug development, virology, and clinical models will be necessary.

The uniqueness of this JEDI GrandChallenge is to push for (i) modelling a number of active molecules at a scale never seen before (ii) the determination of a highly accurate list of active compounds cross-correlated by the teams all over the world, (iii) ultra fast-track in-vitro identification of molecules with 99% reduction in viral activity and (iv) identifying the best drug-cocktail out of existing FDA-approved molecules.

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