ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery
Albert Bou,
Morgan Thomas,
Sebastian Dittert
et al.
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 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,… Show more
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