Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

• URL: http://arxiv.org/abs/2006.02431v1
• Date: Thu, 28 May 2020 01:33:07 GMT
• Title: Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
• Authors: Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner
• Abstract summary: This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.
• Score: 8.090016327163564
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.

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