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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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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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Advances in machine intelligence-driven virtual screening approaches for big-data.

Neeraj Kumar1,2, Vishal Acharya1,2

  • 1Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India.

Medicinal Research Reviews
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Summary
This summary is machine-generated.

Virtual screening (VS), using machine intelligence, accelerates drug discovery by efficiently screening large compound libraries. This review details modern AI-driven VS approaches for faster, more accurate hit identification.

Keywords:
big‐datahit identificationhit optimizationintegrated virtual screeningmachine intelligencemachine intelligence‐driven virtual screeningvirtual screening

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Artificial intelligence in medicine

Background:

  • Virtual screening (VS) is a cornerstone of modern drug discovery, traditionally divided into ligand-based (LB) and structure-based (SB) methods.
  • The increasing volume of chemical and biological data necessitates advanced computational approaches for efficient hit molecule identification.
  • Machine intelligence (MI) offers powerful tools to enhance the speed and accuracy of VS, reducing time and resource consumption.

Purpose of the Study:

  • To review and categorize various VS techniques, highlighting the integration of machine intelligence.
  • To detail the implementation of machine learning and modern AI approaches in VS.
  • To discuss the limitations of current VS methods and explore future prospects.

Main Methods:

  • Review of literature on traditional LB and SB VS approaches.
  • Analysis of machine learning and AI algorithm applications in VS.
  • Categorization of integrated LB/SB techniques and big data-driven VS strategies.

Main Results:

  • MI-driven VS enables rapid screening of ultra-large libraries, significantly reducing hit identification time.
  • Integrated LB/SB approaches enhance prediction accuracy by considering both ligand and target properties.
  • Advanced AI methods are crucial for handling big data and minimizing false positives in drug discovery.

Conclusions:

  • Machine intelligence is revolutionizing virtual screening, making it a more progressive technology in drug discovery.
  • The future of VS lies in advanced, intelligent solutions capable of managing big data and optimizing hit/lead identification.
  • Continued development in AI and computational architecture is essential for overcoming current limitations and improving VS efficacy.