UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909622885941248 |
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| author | Kong, Xiangzhe Zhang, Zishen Zhang, Ziting Jiao, Rui Ma, Jianzhu Huang, Wenbing Liu, Kai Liu, Yang |
| author_facet | Kong, Xiangzhe Zhang, Zishen Zhang, Ziting Jiao, Rui Ma, Jianzhu Huang, Wenbing Liu, Kai Liu, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design Kong, Xiangzhe Zhang, Zishen Zhang, Ziting Jiao, Rui Ma, Jianzhu Huang, Wenbing Liu, Kai Liu, Yang Machine Learning Biomolecules 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. |
| title | UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2503.19300 |