Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules
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| Format: | Preprint |
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2026
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| _version_ | 1866911399588921344 |
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| author | Islam, Naeyma N. Caulfield, Thomas R. |
| author_facet | Islam, Naeyma N. Caulfield, Thomas R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18716 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules Islam, Naeyma N. Caulfield, Thomas R. Artificial Intelligence Biomolecules 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. |
| title | Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules |
| topic | Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2601.18716 |