用对比学习生成可合成药物分子，提升靶点结合预测精度
Structure-guided molecular design with contrastive 3D protein-ligand learning
- 通过等变Transformer对比学习编码蛋白-配体三维结构
- 生成分子在多个靶点上预测结合性能优异且可合成
- 适合需要高效设计新药的科研人员使用
基于结构的药物发现面临两大挑战：准确捕捉三维蛋白-配体相互作用，并在超大规模化学空间中筛选出可合成的候选分子。本文提出统一框架，结合对比3D结构编码与基于商业化合物空间的自回归分子生成。首先，引入SE(3)-等变Transformer，通过对比学习将配体与口袋结构编码至共享嵌入空间，在零样本虚拟筛选中表现优异。其次，将这些嵌入融入多模态化学语言模型（MCLM），根据口袋或配体结构生成目标特异性分子，通过学习到的数据集标记引导输出至目标化学空间，生成候选分子在多个靶点上均展现出良好的预测结合性能。
原文摘要 · Abstract (English)
Structure-based drug discovery faces the dual challenge of accurately capturing 3D protein-ligand interactions while navigating ultra-large chemical spaces to identify synthetically accessible candidates. In this work, we present a unified framework that addresses these challenges by combining contrastive 3D structure encoding with autoregressive molecular generation conditioned on commercial compound spaces. First, we introduce an SE(3)-equivariant transformer that encodes ligand and pocket structures into a shared embedding space via contrastive learning, achieving competitive results in zero-shot virtual screening. Second, we integrate these embeddings into a multimodal Chemical Language Model (MCLM). The model generates target-specific molecules conditioned on either pocket or ligand structures, with a learned dataset token that steers the output toward targeted chemical spaces, yielding candidates with favorable predicted binding properties across diverse targets.
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