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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...
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.
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...
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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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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
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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...

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

Hongma Sun1

  • 1Department of Discovery Chemistry, Hoffmann-La Roche, Inc., Nutley, NJ 07110, USA. hongmao.sun@roche.com

Current Medicinal Chemistry
|April 9, 2008
PubMed
Summary

Virtual screening (VS) is a key cheminformatics tool for drug discovery. This study compares VS with high-throughput screening (HTS) and evaluates pharmacophore-based VS strategies.

Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Molecular modeling

Background:

  • Virtual screening (VS) is integral to cheminformatics and molecular modeling.
  • Abundant 3D protein structures and compound databases (e.g., ZINC) facilitate VS.
  • Advancements in technology enable sophisticated pharmacophore models and large-scale compound screening.

Purpose of the Study:

  • To compare the advantages and disadvantages of VS against experimental high-throughput screening (HTS).
  • To evaluate pharmacophore-based VS against docking-based VS.
  • To outline strategies for successful pharmacophore-based VS.

Main Methods:

  • Examining and comparing VS with HTS.
  • Evaluating pharmacophore-based VS against docking-based VS.

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  • Outlining strategies for pharmacophore model development and optimization.

    • Main Results:

    • The paper details strategies for efficient conformational database enumeration.
    • It covers selecting chemical features and incorporating excluded volumes for pharmacophore models.
    • Successful examples of pharmacophore-based VS are presented.

    Conclusions:

    • Virtual screening is poised to become increasingly vital in future drug discovery.
    • Pharmacophore-based VS offers a powerful approach when optimized effectively.
    • Understanding VS strategies enhances its utility in identifying potential drug candidates.