DrugGen 2：一種疾病感知型語言模型以增強藥物發現

摘要

当前用于药物设计的计算方法通常专注于生成以特定靶点或通用分子性质为条件的分子，往往忽略了疾病背景对靶点行为及治疗效果的影响。为弥补这一不足，我们提出DrugGen-2——一种新型生成模型，能够同时以疾病本体论和靶点蛋白序列为条件设计小分子。DrugGen-2通过在包含已批准药物及其关联疾病与靶点的精选数据集上微调预训练的GPT-2模型开发而成，采用了两阶段策略：先进行监督微调，再通过基于群体的相对策略优化（GRPO）进行强化学习。这一过程由优化化学有效性、新颖性、多样性及高预测结合亲和度的奖励函数指引。在针对糖尿病肾病相关的五个蛋白靶点进行评估时，DrugGen-2显著优于基线模型（DrugGPT与DrugGen）。它展现出更强的生成独特分子能力，与已批准药物具有更高的结构相似性，并在所有靶点上实现了更优的预测结合亲和度。分子对接分析进一步佐证了这些发现，识别出具有强结合潜力的候选配体，其中包括预测亲和度（-9.917、-9.485及-9.367）超过参考药物（如血管紧张素转换酶抑制剂依那普利的-8.283）的化合物。通过将疾病特异性背景融入分子生成过程，DrugGen-2推动了AI辅助药物发现的发展，为考虑疾病与分子靶点间复杂相互作用的从头药物设计及药物重定位提供了强大工具。

English

Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes. To address this gap, we introduce DrugGen-2, a novel generative model that designs small molecules conditioned on both disease ontology and target protein sequences. DrugGen-2 was developed by fine-tuning a pre-trained GPT-2 model on a curated dataset of approved drugs linked to their diseases and targets, using a two-step strategy of supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO). This process was guided by reward functions optimizing for chemical validity, novelty, diversity, and high predicted binding affinity. When evaluated on five protein targets relevant to diabetic nephropathy, DrugGen-2 significantly outperformed baseline models (DrugGPT and DrugGen). It demonstrated a superior capacity to generate unique molecules, exhibited greater structural similarity to approved drugs, and achieved improved predicted binding affinities across all targets. Molecular docking analyses further supported these findings, identifying candidate ligands with strong binding potential, including compounds with predicted affinities (-9.917, -9.485, and -9.367) exceeding those of reference drugs such as enalapril for angiotensin-converting enzyme (-8.283). By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.