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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.
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...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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...
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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VSEPR Theory for Determination of Electron Pair Geometries
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Predictivity of QSAR.

Romualdo Benigni1, Cecilia Bossa

  • 1Environment and Health Department, Istituto Superiore di Sanita', Viale Regina Elena 299, 00161 Rome, Italy. rbenigni@iss.it

Journal of Chemical Information and Modeling
|April 23, 2008
PubMed
Summary
This summary is machine-generated.

Local quantitative structure-activity relationships (QSARs) for mutagenicity and carcinogenicity show good external prediction accuracy, especially for discriminating active from inactive chemicals. Internal validation methods, however, do not reliably predict external performance.

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Medicinal Chemistry

Background:

  • Quantitative Structure-Activity Relationships (QSARs) are crucial for predicting chemical toxicity.
  • Assessing the external predictive power of QSAR models is essential for reliable application.
  • Internal validation metrics may not accurately reflect real-world predictive performance.

Purpose of the Study:

  • To evaluate the external predictive accuracy of local QSAR models for mutagenicity and carcinogenicity.
  • To compare the reliability of internal validation versus external testing for QSARs.
  • To investigate the performance of nonlocal models for noncongeneric chemicals.

Main Methods:

  • External validation using independent test sets of chemicals.
  • Assessment of QSAR models for predicting chemical potency and activity discrimination.
  • Analysis of internal statistical validation methods.
  • Consideration of nonlocal QSAR models for diverse chemical sets.

Main Results:

  • QSARs for potency achieved 30-70% correct predictions on external data.
  • QSARs for activity discrimination showed 70-100% correct external predictions.
  • Internal validation metrics showed poor correlation with actual external predictivity.
  • Applicability domain definition is critical for nonlocal models.

Conclusions:

  • QSARs discriminating between active and inactive chemicals are reliable for practical use.
  • External validation is superior to internal validation for assessing QSAR model reliability.
  • Careful definition of the applicability domain is necessary for QSAR models, particularly nonlocal ones.