PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models

TL;DR

This paper introduces a deep learning framework combining multimodal affinity prediction and reinforcement learning to generate antiviral drug candidates targeting SARS-CoV-2 proteins, demonstrating promising results in ligand generation and synthetic accessibility.

Contribution

It presents a novel conditional generative model integrating affinity and toxicity predictors for targeted antiviral drug design against SARS-CoV-2.

Findings

Generated ligands show an 83% increase in binding affinity bias.

Framework successfully targets unseen SARS-CoV-2 proteins.

Case study identifies potential Envelope-protein inhibitors.

Abstract

With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets. First, we train a multimodal ligand--protein binding affinity model on predicting affinities of antiviral compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator (consisting of two VAEs), we showcase a framework that navigates the chemical space toward regions with more antiviral molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep RL, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling more binding ligands, with an average increase of 83% comparing to an unbiased VAE. We present a case-study on a potential Envelope-protein inhibitor and perform a synthetic accessibility assessment of the best generated molecules is performed that resembles a viable roadmap towards a rapid in-vitro evaluation of potential SARS-CoV-2 inhibitors.