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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
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The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Mia Yang Ang1,2,3, Siew Woh Choo3,4,5,6

  • 1Department of Biomedical Sciences, Sir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Sunway City, Petaling Jaya 47500, Selangor, Malaysia.

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|April 27, 2026
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Summary

Big data technologies and artificial intelligence (AI) are revolutionizing natural product (NP) drug discovery by enabling systematic genome mining and predictive modeling. This data-driven approach accelerates the identification and development of novel therapeutic compounds.

Keywords:
big data analyticschemoinformaticsgenome miningnatural productssustainable drug development

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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Bioinformatics

Background:

  • Natural products (NPs) are historically vital drug scaffolds.
  • Traditional bioprospecting suffers from high rediscovery rates and attrition.
  • Big data technologies offer a shift towards systematic, data-driven NP research.

Purpose of the Study:

  • To review how AI, ML, and multi-omics datasets accelerate natural product research.
  • To examine the integration of computational approaches in drug discovery.
  • To highlight the potential of big data in overcoming NP research limitations.

Main Methods:

  • Genome mining using platforms like antiSMASH for biosynthetic gene clusters.
  • Cheminformatics for predicting structure-activity relationships and ADMET properties.
  • Metabolomics-guided dereplication for prioritizing novel bioactive scaffolds.

Main Results:

  • AI and multi-omics enable in silico lead optimization.
  • Discovery of cryptic metabolites from previously inaccessible microbial taxa.
  • Accelerated identification of novel bioactive scaffolds.

Conclusions:

  • The synergy of big data and NP research is redefining drug development.
  • Computational NP research is a cornerstone of next-generation drug discovery.
  • Despite challenges, these advances promise to accelerate clinical translation.