Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules

TL;DR

This paper introduces a novel AI-driven method for designing molecular glues that target Abeta-42 for degradation, combining structure-based modeling, ADMET screening, and a specialized generative model to create potential therapeutics for Alzheimer's disease.

Contribution

It develops a Ligase-Conditioned Junction Tree VAE that generates ligase-specific molecular glues, integrating protein embeddings and molecular graphs for targeted drug design.

Findings

The generative model produces chemically valid, novel molecules.

Molecules are target-specific and capable of promoting Abeta-42 degradation.

The approach offers a new framework for designing UPS-targeted neurodegenerative therapies.

Abstract

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.