  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.
