IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
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Abstract (arXiv)
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and 2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2020.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 1.
6 papers.
- 2021Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulationscited by 8
- 2021Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computerscited by 7
- 2022AI-coupled HPC Workflowscited by 2
- 2021Optimal Decision Making in High-Throughput Virtual Screening Pipelinescited by 0
- 2022Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflowscited by 0
- 2024Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNNcited by 0
- 2024Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN29 Jul 2024
- 2022AI-coupled HPC Workflows24 Aug 2022
- 2022Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows23 Aug 2022
- 2021Optimal Decision Making in High-Throughput Virtual Screening Pipelines23 Sep 2021
- 2021Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations10 Apr 2021
- 2021Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers4 Mar 2021