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Related Concept Videos

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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-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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Explainable Artificial Intelligence: A Perspective on Drug Discovery.

Yazdan Ahmad Qadri1, Sibhghatulla Shaikh2,3, Khurshid Ahmad4

  • 1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

Pharmaceutics
|September 27, 2025
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Summary
This summary is machine-generated.

Explainable artificial intelligence (XAI) enhances transparency in AI-driven drug discovery, addressing the "black-box" problem. XAI methods accelerate therapeutic target identification and streamline drug development pipelines.

Keywords:
artificial intelligencedrug discoveryexplainable artificial intelligencemolecular modelingpersonalized medicinetherapeutic innovation

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Artificial intelligence

Background:

  • Artificial intelligence (AI) and deep learning (DL) accelerate drug discovery but suffer from a "black-box" problem, hindering pharmaceutical researcher adoption.
  • Explainable artificial intelligence (XAI) offers a solution by increasing transparency and trust in AI models.

Purpose of the Study:

  • To systematically review XAI principles, methodologies, and tools for drug discovery.
  • To explore XAI applications in accelerating various stages of the drug discovery pipeline.
  • To examine how XAI addresses AI model opacity and its challenges.

Main Methods:

  • Systematic investigation of XAI principles and methodologies.
  • Review of XAI tools, models, and frameworks for drug discovery.
  • In-depth discussion of XAI applications in healthcare and drug development.

Main Results:

  • XAI enhances transparency, trust, and reliability in AI-driven drug discovery.
  • XAI applications span molecular modeling, target identification, ADME prediction, and clinical trial design.
  • XAI effectively bridges the gap between computational predictions and pharmaceutical applications.

Conclusions:

  • XAI is crucial for overcoming the "black-box" nature of AI in drug discovery.
  • Further research is needed to address challenges in deploying XAI methodologies.
  • Staying updated on XAI technologies is vital for improving drug discovery efficiency and clinical impact.