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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Machine Learning in Drug Discovery: A Review.

Suresh Dara1, Swetha Dhamercherla1, Surender Singh Jadav2

  • 1Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India.

Artificial Intelligence Review
|August 16, 2021
PubMed
Summary

This review explores machine learning (ML) in drug discovery, accelerating research and reducing clinical trial costs. ML enhances pharmaceutical data analysis for better decision-making and drug design, despite interpretability challenges.

Keywords:
Artificial intelligenceDigital pathologyDrug discoveryMachine learningPrognostic biomarkersTarget validation

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in medicine
  • Pharmaceutical sciences

Background:

  • Drug discovery is a complex, costly, and time-consuming process.
  • Machine learning (ML) offers potential to streamline and improve various stages of drug development.
  • Current literature highlights ML applications across the pharmaceutical pipeline.

Purpose of the Study:

  • To review feasible literature on ML tools and techniques in drug discovery.
  • To highlight ML's role in accelerating research and reducing costs in clinical trials.
  • To identify challenges and opportunities for ML in pharmaceutical data analysis.

Main Methods:

  • Literature review of ML applications in drug discovery.
  • Analysis of ML techniques in quantitative structure-activity relationship (QSAR) analysis, hit discovery, and de novo drug design.
  • Examination of ML in target validation, biomarker identification, and digital pathology.

Main Results:

  • ML significantly improves decision-making in pharmaceutical data analysis.
  • Applications include QSAR, hit identification, de novo drug design, target validation, and biomarker discovery.
  • ML accelerates research and can reduce risks and expenditures in clinical trials.

Conclusions:

  • ML tools are crucial for advancing drug discovery and development.
  • Addressing ML interpretability challenges is key to wider adoption.
  • Generating robust data is essential for validating ML techniques in clinical settings.