UniMoMo：用于从头设计结合剂的3D分子统一生成建模
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
AI总结：
UniMoMo提出首个统一生成框架，通过块图表示和几何潜在扩散模型，实现用单一模型设计多域3D分子结合剂，在肽、抗体和小分子基准上优于域特定模型。
摘要：
设计靶标特异性分子，如小分子、肽和抗体，对生物学研究和药物发现至关重要。现有的生成方法局限于单域分子，无法满足多样化的治疗需求，也无法利用跨域可迁移性来增强模型性能。在本文中，我们引入了3D分子统一生成建模（UniMoMo），这是首个能够使用单一模型设计多个分子域结合剂的框架。具体而言，UniMoMo将不同分子的表示统一为块图，其中每个块对应一个标准氨基酸或一个分子片段。随后，UniMoMo利用几何潜在扩散模型进行3D分子生成，其特点是通过迭代全原子自编码器将块压缩为潜在空间点，然后进行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，我们的统一框架优于现有的域特定模型，凸显了多域训练的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
