RAPID: MolSSI COVID-19 Biomolecular Simulation Data and Algorithm Consortium

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In response to the growing COVID-19 pandemic, the Molecular Sciences Software Institute (MolSSI) will leverage its position as a neutral commodity resource to help the global computational molecular sciences community quickly provide their scientific data and expertise to address the COVID-19 crisis. The MolSSI is jointly supported by the Office of Advanced Cyberinfrastructure and the Divisions of Chemistry and Materials Research. The centerpieces of this engagement will be (1) a centralized repository for simulation-related data targeting the virus and host proteins and potential pharmaceuticals, and (2) a select set of MolSSI Software Seed Fellowships for Ph.D. students and postdocs targeting COVID-19 related software tools that operate on the data developed in the repository. These two components will enable the biomolecular simulation community to share and utilize key data and other resources to help identify the structural and dynamic characteristics of the host-virus complex to generate potential leads for therapeutics. Although this project is intended to address the acute COVID-19 crisis, in the near term, it also will impact research communities and the next generation of computational molecular scientists in the confrontation and proactive resolution of future world problems.The MolSSI will create and curate a large-scale repository containing: simulation input files (structures, configurations, scripts, Jupyter notebooks) in an organized structure; MD trajectories, analysis tools, and ready models for drug discovery; pointers to preprint servers such as arXiv, bioRxiv, and ChemRxiv on biomolecular simulation research in regards SARS-CoV-2; and DOI services that create citable data. In addition, it will engage the molecular sciences community through a set of Software Fellowships for graduate student and postdocs to carry out software development, such as large-scale MD simulations, design of drug discovery tools such as docking, machine learning for small molecule toxicity predictions, and methods for determining whether new drugs are bioavailable or can be synthesized. Collectively, these resources will speed the identification and development of leads for antiviral drugs, analyzing structural effects of genetic variation in the SARS-CoV-2 virus, and inhibitors that can disrupt protein-protein interactions to viral entry into cells and adherence to surfaces that cause disease spread.This award is being funded by the CARES Act supplemental funds allocated to CISE and MPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.