ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
<|reference_start|>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 \url{https://github.com/acellera/acegen-open} and available for use under the MIT license.<|reference_end|>
@article{bou2024acegen:,
    title={ACEGEN: Reinforcement learning of generative chemical agents for drug
  discovery},
    author={Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro
  Ram'irez, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen
  Ahmad, Vincent Moens, Woody Sherman, Simone Sciabola, Gianni De Fabritiis},
    journal={arXiv preprint arXiv:2405.04657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04657},
    primaryClass={cs.LG cs.AI q-bio.BM}
}