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Updated: May 16, 2026

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Protein Target Prediction and Validation of Small Molecule Compound
Published on: February 23, 2024
Classical Docking to Machine Learning Based Docking: Molecular Docking in Drug Discovery
Partha Pratim Gogoi1, Basanta Singha1, Penlisola Longkumer1
1Department of Chemistry, Nagaland University, Lumami, 798627, India.
Current Topics in Medicinal Chemistry
|May 15, 2026
Summary
Molecular docking predicts how drugs interact with targets, accelerating drug discovery. Advances in AI and computational methods enhance its accuracy and applications in personalized medicine.
Area of Science:
- Computational chemistry and drug discovery.
Background:
- Molecular docking is a key computational method for predicting ligand-receptor interactions.
- It is crucial for virtual screening, lead optimization, and structure-based drug design.
Purpose of the Study:
- To provide a comprehensive overview of molecular docking fundamentals, methods, and applications.
- To discuss contemporary scoring strategies and compare popular docking platforms.
- To highlight the integration of docking with emerging computational techniques.
Main Methods:
- Review of molecular docking principles, including workflow, scoring functions, and docking types (rigid, flexible, ensemble).
- Comparative analysis of docking software (AutoDock, MOE, GOLD, Glide, MVD) with benchmarking data.
- Exploration of integrated approaches combining docking with machine learning, AI, and molecular dynamics.
Main Results:
- Docking significantly reduces experimental costs and speeds up therapeutic candidate identification.
- Various scoring functions (force-field, empirical, knowledge-based, consensus) have distinct advantages and limitations.
- Integration with AI, deep learning, and AlphaFold enhances predictive capabilities and scalability.
Conclusions:
- Molecular docking is a transformative tool in modern drug development, with expanding applications in drug discovery, repurposing, and personalized medicine.
- Addressing challenges like protein flexibility and scoring accuracy is crucial for further advancements.
- Hybrid computational solutions are emerging to improve reliability and accuracy.
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