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.

翻译：近年来，强化学习已成为药物设计领域的重要工具，展现出生成和优化具有特定性质分子的潜力。然而，由于先进强化学习算法的复杂性以及对专用代码的高度依赖，在功能、灵活性、可靠性和效率之间取得平衡仍具挑战。本研究提出了ACEGEN——一个专为生成式药物设计构建的全面且简化的工具包，其基于现代强化学习库TorchRL开发，该库提供了经过充分测试的可复用组件。我们通过与其他已发表的生成建模算法进行基准测试验证了ACEGEN的性能，结果表明其具有相当或更优的表现。我们还展示了ACEGEN在多个药物发现案例中的应用实例。ACEGEN可通过\url{https://github.com/acellera/acegen-open}获取，并依据MIT许可证开放使用。