# IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

arXiv: https://arxiv.org/abs/2010.06574 · HTML: https://mlsanity.com/paper/2010.06574/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations](https://mlsanity.com/paper/2104.04797/) (2021)
- [Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers](https://mlsanity.com/paper/2103.02843/) (2021)
- [AI-coupled HPC Workflows](https://mlsanity.com/paper/2208.11745/) (2022)
- [Optimal Decision Making in High-Throughput Virtual Screening Pipelines](https://mlsanity.com/paper/2109.11683/) (2021)
- [Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows](https://mlsanity.com/paper/2208.11069/) (2022)
- [Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN](https://mlsanity.com/paper/2407.19724/) (2024)

## 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.