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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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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
PubMed
Summary

Artificial intelligence (AI) accelerates computational drug discovery by improving ligand optimization and virtual screening. This review details AI

Keywords:
ADMETArtificial intelligenceDe novo designDeep learningDrug designDrug discoveryDrug repurposingDrug sensitivityHTSLibrary designLigand-based virtual screeningMachine learningQSARQuantum mechanicsStructure-based virtual screeningSynthesis planning

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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.