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Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
Christophe Muller1, Obdulia Rabal1, Constantino Diaz Gonzalez2
1Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France.
Methods in Molecular Biology (Clifton, N.J.)
|November 3, 2021
Summary
Artificial intelligence (AI) accelerates computational drug discovery by improving ligand optimization and virtual screening. This review details AI
Area of Science:
- Computational chemistry and pharmacology
- Artificial intelligence in medicine
- Drug discovery and development
Background:
- Drug discovery is lengthy, costly, and has high failure rates.
- Computational methods aid ligand discovery and optimization.
- Advancements in AI, computing power, and data processing have revolutionized modeling.
Purpose of the Study:
- To review the current state of AI applications in drug discovery.
- To highlight AI's impact on various stages of the drug development pipeline.
Main Methods:
- Review of AI methodologies in computational drug discovery.
- Focus on structure- and ligand-based virtual screening.
- Exploration of AI in library design, drug repurposing, de novo design, and ADMET prediction.
Main Results:
- AI significantly enhances virtual screening and library design.
- AI facilitates drug repurposing and prediction of drug sensitivity.
- AI aids in de novo drug design, synthetic accessibility, and ADMET profiling.
Conclusions:
- AI is a transformative technology in modern drug discovery.
- AI methods offer powerful tools for accelerating ligand identification and optimization.
- The integration of AI promises to reduce the cost and time of drug development.
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