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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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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Drug Clearance: Overview01:06

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Drug elimination refers to drug removal from the body, either through urine or bile, by the kidneys or liver, respectively. A pharmacokinetic parameter, drug clearance, measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the drug's concentration in plasma.
Drug clearance is not limited to renal excretion but encompasses all organs involved in drug elimination, including...
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Factors Affecting Drug Response: Overview01:21

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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Drug Delivery: Overview01:16

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The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
Enteral delivery involves administering drugs directly through swallowing, sublingual placement, or buccal application. Orally administered drugs predominantly navigate the...
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Updated: Sep 18, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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AI-Driven Drug Discovery: A Comprehensive Review.

Fábio J N Ferreira1, Agnaldo S Carneiro1

  • 1Universidade Federal do Pará, R. Augusto Corrêa, 01 - Guamá, Belém, Pará 66075-110, Brazil.

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|June 23, 2025
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Artificial intelligence (AI) and machine learning (ML) are revolutionizing drug discovery by improving efficiency and success rates. This review details AI/ML applications from target identification to clinical development, highlighting challenges and future directions for creating better medicines.

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Biology
  • Artificial Intelligence

Background:

  • Traditional drug discovery faces significant hurdles including high costs, long timelines, and low success rates.
  • Artificial intelligence (AI) and machine learning (ML) present promising solutions to overcome these challenges.
  • Recent advancements (2019-2024) show AI/ML's potential across the drug discovery pipeline.

Purpose of the Study:

  • To critically analyze recent AI/ML methodologies in drug discovery (2019-2024).
  • To examine AI applications in key stages: target identification, lead discovery, optimization, and safety assessment.
  • To identify challenges and propose future directions for AI integration in pharmaceutical R&D.

Main Methods:

  • Comprehensive literature review of AI/ML advancements in drug discovery.
  • Analysis of diverse AI techniques: deep learning, graph neural networks, transformers.
  • Comparative assessment of AI approaches, focusing on data quality, validation, and ethics.

Main Results:

  • AI/ML techniques show significant promise in accelerating various stages of drug discovery.
  • Key applications identified in target identification, lead generation, and preclinical safety.
  • Limitations include data accessibility, model interpretability, and clinical translation challenges.

Conclusions:

  • AI/ML integration offers transformative potential for developing safer, more effective medicines.
  • Addressing challenges in data, interpretability, and validation is crucial for successful implementation.
  • Future efforts should focus on transparent methodologies and ethical frameworks for responsible AI adoption.