用肽类分子和蛋白口袋信息生成类肽小分子药物。
Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding
- 基于图神经网络与扩散模型，结合肽与蛋白环境生成小分子。
- 生成分子与原始肽类结构相似，且在非自回归任务中表现领先。
- 支持部分扩散优化，适合药物设计与类肽分子研发者使用。
基于结构的药物设计因人工智能的引入取得显著进展，尤其在先导化合物生成方面。然而，多数AI方法忽略了内源性蛋白与肽类分子的相互作用，可能导致分子设计不佳。本文提出Peptide2Mol，一种E(3)-等变图神经网络扩散模型，通过参考原始肽类配体及其周围蛋白口袋环境，生成小分子。该模型在大规模数据集上训练，采用先进建模技术，在非自回归生成任务中达到当前最优性能，并生成与原始肽类结构相似的小分子。此外，模型可通过部分扩散过程实现分子优化与类肽分子设计。结果表明，Peptide2Mol是一种高效生成与优化生物活性小分子的深度生成模型，适用于从蛋白结合口袋出发的药物发现。
原文摘要 · Abstract (English)
Structure-based drug design has seen significant advancements with the integration of artificial intelligence (AI), particularly in the generation of hit and lead compounds. However, most AI-driven approaches neglect the importance of endogenous protein interactions with peptides, which may result in suboptimal molecule designs. In this work, we present Peptide2Mol, an E(3)-equivariant graph neural network diffusion model that generates small molecules by referencing both the original peptide binders and their surrounding protein pocket environments. Trained on large datasets and leveraging sophisticated modeling techniques, Peptide2Mol not only achieves state-of-the-art performance in non-autoregressive generative tasks, but also produces molecules with similarity to the original peptide binder. Additionally, the model allows for molecule optimization and peptidomimetic design through a partial diffusion process. Our results highlight Peptide2Mol as an effective deep generative model for generating and optimizing bioactive small molecules from protein binding pockets.
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