Accelerating Molecular Glue Optimization/Prioritization Using a Computational Workflow

Molecular glues are transforming targeted protein degradation but optimization remains complex and resource-intensive. This poster presents an integrated computational chemistry workflow combining generative AI and physics-based modelling to enable faster, more rational molecular glue design and prioritization.

Accelerating Molecular Glue Optimization/Prioritization Using a Computational Workflow

By integrating virtual screening, molecular dynamics, and quantum mechanics approaches, this workflow enables structure-guided optimization of molecular glues, improving binding interactions, stability, and prioritization confidence.

The result is a systematic, scalable approach to accelerate molecular glue discovery beyond traditional trial-and-error approaches.

What the Poster Covers

Integrated computational workflow for molecular glues
Combining generative AI, docking, MD simulations and QM analysis

Rational glue optimisation strategy
Structure-guided design targeting key protein–protein interaction interfaces

Case study: DDB1–CDK12–Cyclin K complex
Application of the workflow to optimise and prioritise glue candidates

Advanced modelling techniques
Use of MMPBSA, FMO and electrostatic complementarity analysis

AI-driven compound prioritisation
Identification of optimised candidates with improved stability and binding profiles

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