# Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning

arXiv: https://arxiv.org/abs/2012.01736 · HTML: https://mlsanity.com/paper/2012.01736/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [A Text-guided Protein Design Framework](https://mlsanity.com/paper/2302.04611/) (2023)
- [ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning](https://mlsanity.com/paper/2506.07459/) (2025)
- [Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates](https://mlsanity.com/paper/2105.10489/) (2021)
- [T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy](https://mlsanity.com/paper/2303.02162/) (2023)

## Abstract (arXiv)

The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use of Policy Gradients reduces the compute budget needed to reach consistent, high-quality designs by at least an order of magnitude compared to standard methods. Complexes designed by this method have been validated by molecular dynamics simulations, confirming their increased stability. This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.