Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
This work is significant for researchers in drug discovery, offering a faster computational method for designing peptide-based therapeutics, specifically for COVID-19.
This paper addresses the challenge of designing a novel human angiotensin-converting enzyme 2 (ACE2) variant that binds more tightly to the SARS-CoV-2 spike protein. By formulating protein design as a reinforcement learning problem, the authors achieved consistent, high-quality designs with an order of magnitude reduction in compute budget compared to standard methods.
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.