ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
This provides a more accessible and efficient toolkit for researchers in drug discovery, though it appears incremental as it builds on existing RL libraries.
The authors tackled the challenge of balancing capabilities, flexibility, reliability, and efficiency in reinforcement learning for drug design by introducing ACEGEN, a streamlined toolkit built on TorchRL, which showed comparable or improved performance in benchmarks and was applied in multiple drug discovery case studies.
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.