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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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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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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...

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

Maximum-score diversity selection for early drug discovery.

Thorsten Meinl1, Claude Ostermann, Michael R Berthold

  • 1Nycomed Chair for Bioinformatics and Information Mining, University of Konstanz, Konstanz, Germany. Thorsten.Meinl@uni-konstanz.de

Journal of Chemical Information and Modeling
|February 12, 2011
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Summary

Maximum-Score Diversity Selection enhances drug discovery by considering molecular activity alongside structural diversity. This computationally efficient method yields comparable results to existing approaches, improving early-stage compound selection.

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Published on: December 1, 2020

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Traditional diversity selection in drug discovery prioritizes structural variety, often neglecting crucial activity data.
  • Ignoring molecular activity during selection can lead to suboptimal compound libraries in early drug discovery.

Purpose of the Study:

  • To introduce Maximum-Score Diversity Selection, a novel approach integrating predicted molecular activity into diversity selection.
  • To develop a computationally efficient heuristic method for diversity selection that accounts for both structural and activity properties.

Main Methods:

  • Developed a Maximum-Score Diversity Selection algorithm, a heuristic approach to address the NP-hard nature of optimal selection.
  • Compared the performance of the new method against existing diversity selection techniques.

Main Results:

  • The Maximum-Score Diversity Selection method demonstrates computational efficiency compared to traditional approaches.
  • The heuristic method achieves comparable results to optimal solutions, effectively balancing structural and activity considerations.
  • Validation across multiple datasets confirms the practical utility and effectiveness of the proposed method.

Conclusions:

  • Maximum-Score Diversity Selection offers a more effective strategy for early drug discovery by incorporating activity predictions.
  • The developed heuristic approach provides a computationally feasible solution for optimizing compound library selection.
  • This method represents a significant advancement in selecting diverse and potentially active molecules for drug development.