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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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Structure-Activity Relationships and Drug Design01:28

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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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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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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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Artificial intelligence in drug development.

Kang Zhang1,2, Xin Yang3, Yifei Wang3

  • 1Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Institute for clinical Data Science, Wenzhou Medical University, Wenzhou, China. kang.zhang@gmail.com.

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Artificial intelligence (AI) is transforming drug development by enhancing efficiency and effectiveness. This overview details AI applications from target identification to post-market surveillance, addressing current challenges and future research.

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Area of Science:

  • Pharmacology and Pharmaceutical Sciences
  • Computational Biology and Bioinformatics
  • Artificial Intelligence in Medicine

Background:

  • Traditional drug development is lengthy, costly, and relies heavily on empirical methods.
  • Emerging artificial intelligence (AI) technologies, including large language models (LLMs) and generative AI, offer transformative potential.
  • AI integration is already yielding improvements in drug development efficiency and effectiveness.

Purpose of the Study:

  • To provide a comprehensive overview of recent AI advancements in drug development.
  • To examine AI applications across the entire drug development pipeline.
  • To identify challenges and future research directions for AI-augmented drug development.

Main Methods:

  • Review of recent literature on AI applications in drug development.
  • Analysis of AI integration across key stages: target identification, drug discovery, preclinical studies, clinical trials, and post-market surveillance.
  • Critical examination of current challenges and future research opportunities.

Main Results:

  • AI demonstrates significant potential to enhance efficiency and effectiveness in drug discovery and development.
  • AI applications span the entire workflow, from initial target identification to post-market surveillance.
  • Subtle yet meaningful enhancements have already been observed with AI integration.

Conclusions:

  • AI, particularly LLMs and generative AI, is revolutionizing the drug development paradigm.
  • Addressing current challenges is crucial for unlocking the full potential of AI in pharmaceutical innovation.
  • Future research should focus on further integrating and optimizing AI tools for accelerated and improved drug development.