Core Insights - The article discusses the development of a unified generative framework called UniMoMo, which aims to design various types of molecules for drug development by utilizing molecular fragments for unified representation [1][3][8]. Group 1: Framework Overview - UniMoMo is a collaborative effort between Tsinghua University, Renmin University, and ByteDance's AI drug development team, focusing on generating different types of binding molecules for the same target [1][2]. - The framework employs a variational autoencoder to compress the full atomic conformations of molecular fragments and utilizes geometric diffusion modeling in the compressed latent space [1][8]. Group 2: Need for Unified Modeling - Different molecular types have distinct advantages and are suitable for various disease scenarios, necessitating the selection of the most appropriate type for specific therapeutic needs [3][4]. - Existing generative methods typically model only one type of molecule, limiting their ability to meet diverse treatment requirements and leverage commonalities across different molecular types [3][4]. Group 3: Challenges in Generative Modeling - The framework faces significant challenges in molecular representation and the design of generative algorithms due to the structural differences among molecular types [6][7]. - A key challenge is maintaining both atomic-level geometric details and abstracting structural hierarchies in the unified molecular representation [6][7]. Group 4: Design and Performance of UniMoMo - UniMoMo employs a unified representation of all molecular types as graphs composed of molecular fragments, allowing for the retention of atomic-level details and hierarchical structures [8][9]. - The iterative variational autoencoder (IterVAE) compresses all atoms in each block into a point in the latent space, facilitating efficient generative modeling while maintaining structural accuracy [9][11]. Group 5: Evaluation and Results - UniMoMo has been evaluated across multiple structural design tasks, demonstrating superior performance compared to existing single-type generative models in terms of geometric modeling capabilities and cross-modal generalization [11][12]. - In peptide design tasks, UniMoMo achieved significant improvements in key metrics such as structural accuracy and interaction quality, outperforming models like RFDiffusion and PepFlow [12][13]. Group 6: Future Directions - The authors suggest that future work could expand the modeling to include non-natural amino acids and more complex drug forms, enhancing the candidate molecular space [21]. - The unified modeling approach also presents opportunities for improving the controllability and interpretability of generative models, potentially advancing the development of reliable and practical molecular design platforms [21].
入选ICML 2025，清华/人大/字节提出首个跨分子种类统一生成框架UniMoMo，实现多类型药物分子设计
3 6 Ke·2025-05-28 10:30