Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.
Acellera Labs, C Dr. Trueta 183, 08005, Barcelona, Spain.
J Chem Inf Model. 2024 Aug 12;64(15):5900-5911. doi: 10.1021/acs.jcim.4c00895. Epub 2024 Aug 2.
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 (RL) 已成为药物设计中一种有价值的工具，为提出和优化具有理想性质的分子提供了可能。然而，由于先进的 RL 算法的复杂性以及对专门代码的高度依赖，在能力、灵活性、可靠性和效率之间取得平衡仍然具有挑战性。在这项工作中，我们引入了 ACEGEN，这是一个针对生成性药物设计量身定制的全面而精简的工具包，它是使用 TorchRL 构建的，TorchRL 是一个现代的 RL 库，提供了经过充分测试的可重用组件。我们通过与其他已发表的生成建模算法进行基准测试来验证 ACEGEN，并展示了可比或改进的性能。我们还展示了 ACEGEN 在多个药物发现案例研究中的应用示例。ACEGEN 可在 https://github.com/acellera/acegen-open 上访问，并可根据麻省理工学院的许可证使用。
