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Related Concept Videos

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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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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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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Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Artificial intelligence as a tool in drug discovery and development.

Maria Kokudeva1, Mincho Vichev2, Emilia Naseva3

  • 1Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, Sofia 1000, Bulgaria. kokudeva.mariya@gmail.com.

World Journal of Experimental Medicine
|September 23, 2024
PubMed
Summary

Artificial intelligence (AI) offers transformative potential for drug discovery and development by enhancing target identification and lead optimization. Challenges like data privacy and model interpretability require careful consideration for successful pharmaceutical integration.

Keywords:
AI-driven medicineArtificial intelligenceDecision-makingDrug developmentDrug discoveryHealthcarePublic health

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Area of Science:

  • Pharmaceutical Science
  • Computational Biology
  • Biotechnology

Background:

  • Artificial intelligence (AI) is rapidly advancing, with significant implications for the pharmaceutical industry.
  • AI tools, including machine learning and deep learning, can streamline drug discovery and development processes.

Purpose of the Study:

  • To critically examine the feasibility and prospects of integrating AI in pharmaceutical research.
  • To discuss AI's role in target identification, lead optimization, and predictive modeling.

Main Methods:

  • Review of current AI-driven approaches in drug development.
  • Analysis of integrating omics data, electronic health records, and chemical informatics.
  • Exploration of challenges and ethical considerations.

Main Results:

  • AI demonstrates potential in accelerating drug repurposing and identifying novel therapeutic targets.
  • Integration of diverse datasets enhances AI's predictive capabilities.
  • Challenges include data privacy, model interpretability, and clinical validation.

Conclusions:

  • AI presents a transformative opportunity for drug discovery, but requires addressing significant challenges.
  • Further research and ethical guidelines are necessary to fully harness AI's potential in pharmaceuticals.