ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery

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 https://github.com/acellera/acegen-open and available for use under the MIT license.

近年来，强化学习（Reinforcement Learning, RL）已成为药物研发领域极具价值的工具，具备设计并优化具有期望属性分子的潜力。然而，由于先进强化学习算法的复杂性，以及对专业代码的高度依赖，在性能、灵活性、可靠性与效率之间寻求最优平衡仍颇具挑战。本研究中，我们推出了ACEGEN——一款专为生成式药物设计打造的全面且精简高效的工具包，基于经过充分测试、可复用组件的现代化强化学习库TorchRL开发而成。我们通过与其他已发表的生成式建模算法开展基准测试对ACEGEN进行验证，结果显示其性能可与同类方法媲美，甚至更胜一筹。此外，我们还展示了ACEGEN应用于多项药物研发案例研究的实际示例。ACEGEN可通过https://github.com/acellera/acegen-open获取，并采用MIT许可证开源可用。