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Updated: Jun 5, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity
Shuo Zhang1,2, Li Xie1, Daniel Tiourine1
1Department of Computer Science, Hunter College, The City University of New York, New York, 10065, NY, USA.
ProMoNet, a novel sequence-based framework, enhances protein-ligand binding prediction by modeling microscale interactions and protein dynamics. This approach accelerates drug discovery by improving predictions of binding affinity and kinetics.
Area of Science:
- Computational biology
- Drug discovery
- Bioinformatics
Background:
- Predicting protein-ligand binding is crucial for drug discovery.
- Existing methods struggle with data integration, protein dynamics, and microscale interactions.
Purpose of the Study:
- Introduce ProMoNet, a sequence-based pre-training and fine-tuning framework.
- Enhance prediction of protein-ligand binding characteristics like affinity and kinetics.
Main Methods:
- Leveraged protein and molecular foundation models for expanded data coverage.
- Developed a pre-training module using protein-ligand binding site prediction.
- Designed a fine-tuning module to transfer pre-trained knowledge.
Main Results:
- Pre-training module effectively models microscale interactions and protein dynamics, including cryptic binding sites.
- Outperformed or matched state-of-the-art structure-based methods in binding site identification.
- Achieved superior performance in predicting binding affinity and kinetics.
Conclusions:
- ProMoNet offers a highly efficient and effective sequence-based approach for protein-ligand binding prediction.
- Demonstrated potential for broad applications in accelerating drug discovery.
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