一个模型统一生成小分子、肽和抗体，助力新药设计
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
- 将不同分子统一为块图结构，用几何扩散模型生成3D构型
- 在多类分子上优于专用模型，证明跨域训练有效
- 适合药物发现中需多类型配体设计的研究者
靶向分子（如小分子、肽和抗体）的设计对生物研究和药物发现至关重要。现有生成方法局限于单一分子类型，无法满足多样治疗需求或利用跨域迁移提升性能。本文提出首个统一生成多类型分子的框架UniMoMo，能以单一模型设计多种分子。UniMoMo将不同分子表示为块图结构，每个块对应标准氨基酸或分子片段，并采用几何潜在扩散模型生成3D结构：通过迭代全原子自编码器将块压缩至潜在空间，再进行E(3)-等变扩散过程。在肽、抗体和小分子上的广泛基准测试表明，该统一框架优于现有领域专用模型，凸显多域训练的优势。
原文摘要 · Abstract (English)
The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restricted to single-domain molecules, failing to address versatile therapeutic needs or utilize cross-domain transferability to enhance model performance. In this paper, we introduce Unified generative Modeling of 3D Molecules (UniMoMo), the first framework capable of designing binders of multiple molecular domains using a single model. In particular, UniMoMo unifies the representations of different molecules as graphs of blocks, where each block corresponds to either a standard amino acid or a molecular fragment. Subsequently, UniMoMo utilizes a geometric latent diffusion model for 3D molecular generation, featuring an iterative full-atom autoencoder to compress blocks into latent space points, followed by an E(3)-equivariant diffusion process. Extensive benchmarks across peptides, antibodies, and small molecules demonstrate the superiority of our unified framework over existing domain-specific models, highlighting the benefits of multi-domain training.
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