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.

翻译：阿尔茨海默病（AD）的特征是淀粉样蛋白β-42（Abeta-42）的病理性积累，导致突触功能障碍和神经退行性变。尽管细胞外淀粉样斑块已得到充分研究，但越来越多的证据表明细胞内Abeta-42是疾病进展的早期毒性驱动因素。本研究提出了一种新颖的人工智能辅助药物设计方法，通过利用E3连接酶导向的分子胶，促进泛素-蛋白酶体系统（UPS）对Abeta-42的靶向降解。我们通过基于结构的建模、ADMET筛选和分子对接，系统评估了Abeta-42与三种E3连接酶（CRBN、VHL和MDM2）形成三元复合物的潜力。随后，我们开发了一种连接酶条件化连接树变分自编码器（LC-JT-VAE），该模型结合蛋白质序列嵌入和扭转角感知分子图，以生成连接酶特异性小分子。结果表明，该生成模型能够产生化学结构合理、新颖且具有靶向特异性的分子胶，可有效促进Abeta-42降解。这一集成方法为设计针对神经退行性疾病的UPS靶向疗法提供了一个有前景的框架。