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Related Concept Videos

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.
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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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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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coral Software: QSAR for Anticancer Agents.

Emilio Benfenati1, Andrey A Toropov, Alla P Toropova

  • 1Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy.

Chemical Biology & Drug Design
|March 26, 2011
PubMed
Summary
This summary is machine-generated.

Quantitative Structure-Activity Relationship (QSAR) models were developed using the CORAL software to predict anticancer activity. SMILES-based descriptors showed good predictive performance for naphthyridine derivatives.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Cheminformatics

Background:

  • Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) are crucial for drug discovery.
  • Simplified Molecular Input Line Entry System (SMILES) provides a linear notation for chemical structures.
  • Molecular fragments within SMILES can serve as indicators for predicting properties.

Purpose of the Study:

  • To develop and validate QSAR models for predicting the anticancer activity of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines.
  • To evaluate the performance of CORAL software in establishing structure-activity relationships using SMILES-based descriptors.

Main Methods:

  • Utilized the CORAL (Correlations And Logic) freeware for QSPR/QSAR modeling.
  • Employed Monte Carlo optimization to calculate correlation weights for SMILES-derived molecular fragments.
  • Generated SMILES-based descriptors and correlated them with anticancer activity endpoints.
  • Assessed model predictability using external validation sets with varying training/validation splits.

Main Results:

  • Developed multiple SMILES-based QSAR models for the anticancer activity of naphthyridine compounds.
  • Achieved good predictive performance across different data splits.
  • Reported R-squared (r²) values ranging from 0.778 to 0.829 for sub-training sets.
  • Observed R-squared values between 0.828 and 0.933 for calibration sets.
  • Demonstrated validation set R-squared values from 0.807 to 0.931.

Conclusions:

  • SMILES-based descriptors calculated with CORAL effectively model the anticancer activity of the studied naphthyridine derivatives.
  • The developed QSAR models exhibit robust predictability for external datasets.
  • CORAL software is a valuable tool for establishing quantitative structure-activity relationships.