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A Structure-Based Drug Discovery Paradigm.

Maria Batool1, Bilal Ahmad2, Sangdun Choi3

  • 1Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea. mariabatool.28@gmail.com.

International Journal of Molecular Sciences
|June 9, 2019
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning accelerate structure-based drug design by analyzing vast datasets. These advanced methods enhance lead discovery, improving efficiency and cost-effectiveness in drug development.

Keywords:
artificial intelligencedeep learningneural networkscoring functionstructure-based drug discoveryvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Traditional drug discovery is being augmented by structure-based approaches.
  • Genomic and proteomic data offer numerous new drug targets.
  • Handling large datasets from combinatorial chemistry presents a significant challenge.

Purpose of the Study:

  • To review current methods for structure-based drug design.
  • To highlight the role of artificial intelligence (AI) and deep learning in drug discovery.
  • To focus on AI-driven virtual screening and de novo drug design.

Main Methods:

  • Review of existing literature on structure-based drug design techniques.
  • Analysis of AI and deep learning algorithms applied to drug discovery.
  • Examination of virtual screening and de novo design methodologies.

Main Results:

  • AI and deep learning are crucial for analyzing large biological datasets.
  • Machine learning methods significantly impact medicinal chemistry and drug discovery.
  • Advanced AI tools offer sophisticated approaches to identify and design drug candidates.

Conclusions:

  • AI and deep learning are transforming structure-based drug design.
  • These technologies are essential for managing big data in drug discovery.
  • AI-powered methods enhance the speed and efficiency of identifying novel drug leads.