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arXiv · 2503.19300

UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design

Xiangzhe Kong·Zishen Zhang·Ziting Zhang·Rui Jiao·Jianzhu Ma·Wenbing Huang·Kai Liu·Yang Liu
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Abstract

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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Xiangzhe Kong, Zishen Zhang, Ziting Zhang, Rui Jiao, Jianzhu Ma, Wenbing Huang, Kai Liu, Yang Liu. 2025-05-26. UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design. https://arxiv.org/abs/2503.19300

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