AI设计药物终于开始考虑“你得的是什么病”
以前的AI设计药物，要么只看靶点（比如某个蛋白质），要么只看分子性质，但不管这个靶点是在什么疾病背景下工作的——同一个靶点在不同病里表现可能完全不同。DrugGen-2把“疾病本体”（比如糖尿病肾病）也作为输入条件，让模型同时看“治什么病”和“打什么靶”。它用GPT-2微调，先监督学习再强化学习，优化化学有效性、新颖性、多样性和结合亲和力。在糖尿病肾病相关的5个靶点上，它生成的分子比基线模型更接近已上市药物，预测结合力也更强，有的甚至超过现有药物依那普利。这不是你明天能用的工具，但它指出了AI制药的一个关键盲区：疾病上下文。
📄 原文摘要(英文)
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.