ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

TL;DR

ACEGEN is a new RL toolkit for drug discovery that simplifies generative modeling, achieves competitive performance, and is validated through benchmarks and case studies.

Contribution

It introduces ACEGEN, a streamlined RL toolkit built with TorchRL for generative drug design, improving usability and performance.

Findings

Benchmarking shows ACEGEN's performance is comparable or superior to existing methods.

ACEGEN successfully applied in multiple drug discovery case studies.

Toolkit is open-source and user-friendly.

Abstract

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at \url{https://github.com/acellera/acegen-open} and available for use under the MIT license.