用AI生成可合成的分子胶，靶向降解阿尔茨海默病致病蛋白。
Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules
- 基于三类E3连接酶设计条件生成模型，精准筛选分子胶
- 生成分子可有效促进β-42蛋白降解，且化学结构合理
- 适合神经退行性疾病药物研发者参考
阿尔茨海默病（AD）由β-42（Abeta-42）病理堆积引发，导致突触功能障碍与神经退行性病变。尽管细胞外淀粉样斑块研究较充分，但越来越多证据表明细胞内Abeta-42是疾病早期毒性的关键驱动因素。本研究提出一种AI辅助药物设计新方法，通过E3连接酶导向的分子胶，激活泛素-蛋白酶体系统（UPS）靶向降解Abeta-42。我们采用结构建模、ADMET筛选和分子对接，系统评估了三种E3连接酶（CRBN、VHL、MDM2）与Abeta-42形成三元复合物的潜力。随后开发了配体条件化衔接树变分自编码器（LC-JT-VAE），融合蛋白质序列嵌入与扭转角感知分子图，生成针对特定连接酶的小分子。结果表明，该生成模型可产出化学上有效、新颖且目标特异的分子胶，能促进Abeta-42降解。该整合框架为神经退行性疾病靶向UPS治疗提供了新思路。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is marked by the pathological accumulation of amyloid beta-42 (Abeta-42), contributing to synaptic dysfunction and neurodegeneration. While extracellular amyloid plaques are well-studied, increasing evidence highlights intracellular Abeta-42 as an early and toxic driver of disease progression. In this study, we present a novel, AI-assisted drug design approach to promote targeted degradation of Abeta-42 via the ubiquitin-proteasome system (UPS), using E3 ligase-directed molecular glues. We systematically evaluated the ternary complex formation potential of Abeta-42 with three E3 ligases: CRBN, VHL, and MDM2, through structure-based modeling, ADMET screening, and docking. We then developed a Ligase-Conditioned Junction Tree Variational Autoencoder (LC-JT-VAE) to generate ligase-specific small molecules, incorporating protein sequence embeddings and torsional angle-aware molecular graphs. Our results demonstrate that this generative model can produce chemically valid, novel, and target-specific molecular glues capable of facilitating Abeta-42 degradation. This integrated approach offers a promising framework for designing UPS-targeted therapies for neurodegenerative diseases.
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