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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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Pharmacodynamics: Overview and Principles01:21

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Pharmacodynamics is a scientific field that delves into drugs' intricate biochemical, cellular, and physiological effects on the human body. The study of pharmacodynamics helps us understand how drugs interact with the body and elicit various responses.
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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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Principles of Drug Action01:24

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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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Updated: Jul 2, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Structure and dynamics in drug discovery.

Haixin Wei1, J Andrew McCammon2

  • 1Departments of Chemistry & Biochemistry, and Pharmacology, University of California San Diego, La Jolla, CA, USA. h9wei@ucsd.edu.

Npj Drug Discovery
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

Computing significantly aids structure-based drug discovery, evolving from early structural studies to advanced molecular dynamics simulations and machine learning. This review covers its history, current applications, and future potential in developing new medicines.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Last Updated: Jul 2, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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Published on: May 16, 2021

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Structure-based drug discovery leverages computational methods to identify and optimize drug candidates.
  • Early computational approaches relied on limited experimental structural data.
  • The field has advanced with sophisticated simulation and machine learning techniques.

Purpose of the Study:

  • To provide a historical overview of computational roles in structure-based drug discovery.
  • To examine current trends and methodologies in the field.
  • To explore future directions and potential innovations.

Main Methods:

  • Review of historical computational approaches in drug discovery.
  • Analysis of current molecular dynamics simulations and machine learning applications.
  • Discussion of emerging computational strategies.

Main Results:

  • Demonstrated evolution of computational techniques in drug discovery.
  • Highlighted the increasing importance of advanced simulations and AI.
  • Identified key areas for future research and development.

Conclusions:

  • Computational methods are integral to modern drug discovery.
  • Continued advancements in computing power and algorithms will drive future breakthroughs.
  • Interdisciplinary collaboration is crucial for maximizing computational impact.