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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...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: Jul 4, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
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Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Hunting for predictive computational drug-discovery models.

Christopher D Snow1

  • 1Frances H Arnold Research Group, Department of Chemical Engineering, 1200 E California Boulevard, MC 210-41, Pasadena, CA, USA. csnow@alum.mit.edu

Expert Review of Anti-Infective Therapy
|July 1, 2008
PubMed
Summary

Computer-Aided Drug Design (CADD) is advancing rapidly. This report highlights best practices to enhance predictive accuracy and guide high-throughput screening in drug discovery.

Related Experiment Videos

Last Updated: Jul 4, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Pharmacology

Background:

  • The Keystone Symposium on Computer-Aided Drug Design convened in 2008.
  • It gathered ~180 experts from academia and the pharmaceutical industry.
  • The field of CADD is relatively young and evolving.

Purpose of the Study:

  • To focus on best practices in Computer-Aided Drug Design.
  • To address active debates and avoid pitfalls in the discipline.
  • To improve the reliability and predictive power of CADD models.

Main Methods:

  • Keynote address by pioneer Irwin Kuntz.
  • Discussions on current challenges and advancements in CADD.
  • Focus on practical applications and reliability improvements.

Main Results:

  • Identified key areas for improving CADD reliability.
  • Emphasized the importance of best practices for predictive accuracy.
  • Highlighted the growing role of CADD in optimizing drug discovery pipelines.

Conclusions:

  • Adopting best practices is crucial for advancing CADD.
  • Improved CADD models will increasingly inform high-throughput screening strategies.
  • The field is moving towards more reliable and predictive applications in drug discovery.