使用多GPT智能体的强化学习进行从头药物设计
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
AI总结：
本文提出MolRL-MGPT，一种基于多GPT智能体协作的强化学习算法，用于从头药物分子生成，在GuacaMol基准和SARS-CoV-2抑制剂设计中均取得良好效果。
摘要：
从头药物设计是药理学中的一个关键问题，也是AI for science研究的一个新关注领域。该领域的核心挑战在于生成具有特定性质的分子，同时产生广泛多样的候选分子。尽管变压器模型和强化学习等先进技术已被应用于药物设计，但其潜力尚未被充分挖掘。因此，我们提出了MolRL-MGPT，一种使用多个GPT智能体进行药物分子生成的强化学习算法。为促进分子多样性，我们鼓励智能体协作，在不同方向上搜索理想分子。我们的算法在GuacaMol基准上显示出有前景的结果，并在设计针对SARS-CoV-2蛋白靶点的抑制剂方面表现出有效性。代码可在以下地址获取：https://github.com/HXYfighter/MolRL-MGPT。
英文摘要：
De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/MolRL-MGPT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
