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Updated: May 25, 2025
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
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Recent advances in AI-driven protein-ligand interaction predictions
Jaemin Sim1, Dongwoo Kim2, Bomin Kim2
1Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea.
Current Opinion in Structural Biology
|February 25, 2025
Summary
Artificial intelligence (AI) is revolutionizing structure-based drug discovery by enhancing protein-ligand interaction predictions. AI models improve ligand binding site prediction, pose estimation, and virtual screening accuracy.
Area of Science:
- Computational chemistry
- Artificial intelligence in drug discovery
- Molecular modeling
Background:
- Structure-based drug discovery relies on computational models for protein-ligand interactions.
- Traditional docking methods have limitations in accuracy.
- AI is emerging as a powerful tool to overcome these limitations.
Purpose of the Study:
- To review recent advancements in AI-driven methodologies for protein-ligand interaction prediction.
- To highlight AI's impact on key aspects of structure-based drug discovery.
- To discuss challenges and future directions.
Main Methods:
- Review of AI models including graph neural networks, mixture density networks, transformers, and diffusion models.
- Analysis of AI applications in ligand binding site prediction, binding pose estimation, scoring function development, and virtual screening.
- Integration of geometric deep learning, sequence-based embeddings, and deep learning with physical constraints.
Main Results:
- AI models demonstrate enhanced predictive performance compared to traditional methods.
- AI refines ligand binding site identification and binding pose prediction.
- AI-powered scoring functions improve binding affinity estimation and virtual screening strategies.
Conclusions:
- AI technologies are significantly improving the accuracy and efficiency of structure-based drug discovery.
- Generalization across diverse protein-ligand pairs remains an ongoing challenge.
- AI is poised to revolutionize molecular docking and affinity prediction.
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