Title: Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization Show Chinese title

Title: 协作专家大语言模型引导的多目标分子优化

Abstract: Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error, making multi-objective optimization both time-consuming and resource-intensive. Current AI-based methods have shown limited success in handling multi-objective optimization tasks, hampering their practical utilization. To address this challenge, we present MultiMol, a collaborative large language model (LLM) system designed to guide multi-objective molecular optimization. MultiMol comprises two agents, including a data-driven worker agent and a literature-guided research agent. The data-driven worker agent is a large language model being fine-tuned to learn how to generate optimized molecules considering multiple objectives, while the literature-guided research agent is responsible for searching task-related literature to find useful prior knowledge that facilitates identifying the most promising optimized candidates. In evaluations across six multi-objective optimization tasks, MultiMol significantly outperforms existing methods, achieving a 82.30% success rate, in sharp contrast to the 27.50% success rate of current strongest methods. To further validate its practical impact, we tested MultiMol on two real-world challenges. First, we enhanced the selectivity of Xanthine Amine Congener (XAC), a promiscuous ligand that binds both A1R and A2AR, successfully biasing it towards A1R. Second, we improved the bioavailability of Saquinavir, an HIV-1 protease inhibitor with known bioavailability limitations. Overall, these results indicate that MultiMol represents a highly promising approach for multi-objective molecular optimization, holding great potential to accelerate the drug development process and contribute to the advancement of pharmaceutical research. Show Chinese abstract

Abstract: 分子优化是一个关键但复杂且耗时的过程，通常成为药物开发的瓶颈。 传统方法严重依赖试错法，使得多目标优化既耗时又耗费资源。 目前基于人工智能的方法在处理多目标优化任务方面表现出有限的成功，阻碍了其实际应用。 为了解决这一挑战，我们提出了MultiMol，一个协作式大型语言模型（LLM）系统，旨在指导多目标分子优化。 MultiMol包括两个代理，包括一个数据驱动的工作者代理和一个文献引导的研究代理。 数据驱动的工作者代理是一个经过微调的大型语言模型，旨在学习如何生成考虑多个目标的优化分子，而文献引导的研究代理负责搜索与任务相关的文献，以找到有助于识别最有希望的优化候选物的有用先验知识。 在六个多目标优化任务的评估中，MultiMol显著优于现有方法，成功率达到82.30%，这与当前最强方法的27.50%成功率形成鲜明对比。 为进一步验证其实际影响，我们在两个现实世界挑战中测试了MultiMol。 首先，我们提高了黄嘌呤胺共轭物（XAC）的选择性，这是一种能同时结合A1R和A2AR的多功能配体，成功地将其偏向于A1R。 其次，我们提高了沙奎那韦的生物利用度，这是一种已知生物利用度有限的HIV-1蛋白酶抑制剂。 总体而言，这些结果表明，MultiMol代表了一种高度有前景的多目标分子优化方法，具有加速药物开发过程和推动药物研究发展的巨大潜力。