UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
This addresses the need for versatile therapeutic design in drug discovery by enabling cross-domain molecule generation, representing a novel method for a known bottleneck.
The paper tackles the problem of designing target-specific molecules across multiple domains (small molecules, peptides, antibodies) by introducing UniMoMo, a unified generative framework that outperforms existing domain-specific models in benchmarks, demonstrating the benefits of multi-domain training.
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.