用强化学习优化药物分子，提升疗效同时保持原有特性。
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
- 提出结构化策略优化算法，精准微调药物生成大模型。
- 在百万级化合物数据集上验证，显著提升靶向性能。
- 适合药物研发人员快速迭代候选分子。
微调大型语言模型（LLM）对实现特定目标的生成结果至关重要。本研究聚焦药物优化，提出一种新型强化学习算法以微调基于生成模型的药物优化大模型，在提升原药物在目标属性上的表现的同时，保留其有益化学特性。工作包含两个核心部分：(1) DrugImprover：一个专为提升药物优化鲁棒性和效率设计的框架，包含面向药物优化的LLM及新颖的结构化策略优化（SPO）算法，该算法具有理论基础，通过将生成分子的改进与输入分子在目标属性上对齐，提供微调生成模型的新视角；(2) 构建了一个包含100万种化合物的数据集，每种化合物均包含在5个人类癌症相关蛋白上的OEDOCK对接得分以及24个SARS-CoV-2病毒结合位点的数据。我们全面评估了SPO算法，并证明其在多个目标属性上有效提升原始药物性能。代码与数据集将公开于：https://github.com/xuefeng-cs/DrugImproverGPT。
原文摘要 · Abstract (English)
Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model, enhancing the original drug across target objectives, while retains the beneficial chemical properties of the original drug. This work is comprised of two primary components: (1) DrugImprover: A framework tailored for improving robustness and efficiency in drug optimization. It includes a LLM designed for drug optimization and a novel Structured Policy Optimization (SPO) algorithm, which is theoretically grounded. This algorithm offers a unique perspective for fine-tuning the LLM-based generative model by aligning the improvement of the generated molecule with the input molecule under desired objectives. (2) A dataset of 1 million compounds, each with OEDOCK docking scores on 5 human proteins associated with cancer cells and 24 binding sites from SARS-CoV-2 virus. We conduct a comprehensive evaluation of SPO and demonstrate its effectiveness in improving the original drug across target properties. Our code and dataset will be publicly available at: https://github.com/xuefeng-cs/DrugImproverGPT.
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