ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

arXiv:2405.04657v1 Announce Type: new
Abstract: 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 capability, flexibility, and reliability 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 decision-making library …

agents arxiv cs.ai cs.lg discovery drug discovery generative q-bio.bm reinforcement reinforcement learning type