# Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

arXiv: https://arxiv.org/abs/2103.02843 · HTML: https://mlsanity.com/paper/2103.02843/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 87 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model](https://mlsanity.com/paper/1609.00680/) (2016)
- [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://mlsanity.com/paper/1703.10603/) (2017)
- [DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding](https://mlsanity.com/paper/1909.07817/) (2019)
- [Automated design of collective variables using supervised machine learning](https://mlsanity.com/paper/1802.10510/) (2018)
- [Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms](https://mlsanity.com/paper/2103.00091/) (2021)
- [Reinforced dynamics for enhanced sampling in large atomic and molecular systems](https://mlsanity.com/paper/1712.03461/) (2017)
- [RADICAL-Cybertools: Middleware Building Blocks for Scalable Science](https://mlsanity.com/paper/1904.03085/) (2019)
- [IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads](https://mlsanity.com/paper/2010.06574/) (2020)
- [Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release](https://mlsanity.com/paper/2006.02431/) (2020)
- [When we can trust computers (and when we can't)](https://mlsanity.com/paper/2007.03741/) (2020)
- [Scalable HPC and AI Infrastructure for COVID-19 Therapeutics](https://mlsanity.com/paper/2010.10517/) (2020)

## Built on by (most cited first, 7 of 7)

- [Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations](https://mlsanity.com/paper/2104.04797/) (2021)
- [Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data](https://mlsanity.com/paper/2210.16206/) (2022)
- [Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow](https://mlsanity.com/paper/2210.16248/) (2022)
- [Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows](https://mlsanity.com/paper/2208.11069/) (2022)
- [Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors](https://mlsanity.com/paper/2210.16219/) (2022)
- [Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN](https://mlsanity.com/paper/2407.19724/) (2024)
- [Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides](https://mlsanity.com/paper/2511.00209/) (2025)

## Abstract (arXiv)

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.