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Updated: Jul 2, 2026
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
AI-guided competitive docking for virtual screening and compound efficacy prediction.
Manon Mirgaux1, Valeria Barcelli2, Adeline C Y Chua3
1Unit of Microbiology, Bioorganic and Macromolecular Chemistry, Department of Research in Drug Development, Faculté de Pharmacie, Université Libre de Bruxelles, Brussels, Belgium. manon.mirgaux@ulb.be.
New machine learning models accurately predict protein-ligand interactions and identify active drug compounds. Pairwise competitive docking enhances drug discovery by ranking molecules and accelerating hit identification for more cost-effective development.
Area of Science:
- Computational biology
- Drug discovery
- Structural bioinformatics
Background:
- Machine learning (ML) has advanced protein structure and interaction prediction.
- The application of ML in drug discovery is an evolving field.
- Accurate prediction of protein-ligand interactions is crucial for identifying potential drug candidates.
Purpose of the Study:
- To evaluate the efficacy of denoise diffusion-based co-folding methods for protein-ligand interaction prediction.
- To introduce and validate a novel strategy, pairwise competitive docking, for ranking candidate molecules in drug discovery.
- To demonstrate the potential of ML in accelerating structure-based drug design.
Main Methods:
- Utilized denoise diffusion-based co-folding methods (e.g., AlphaFold3, Boltz-1/2) for predicting protein-ligand interactions.
- Developed and applied pairwise competitive docking to rank molecules based on relative binding affinity.
- Validated the method across 17 diverse protein benchmark systems.
Main Results:
- Denoise diffusion models achieved high accuracy in predicting protein-ligand interactions and distinguishing active from inactive compounds.
- Pairwise competitive docking generated rankings consistent with experimental trends, with concordance indices ranging from 0.52 to 0.89.
- The method demonstrated strong agreement with existing affinity prediction tools (Boltz-2) and accelerated hit identification in large chemical libraries.
Conclusions:
- Modern ML models, including pairwise competitive docking, offer a faster, more reliable, and cost-effective approach to structure-based drug design.
- The developed method serves as a practical alternative for prioritizing potential inhibitors.
- Pairwise competitive docking can guide the de novo design of potent inhibitors, improving drug discovery workflows.
