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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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Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...
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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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Cholinergic agonists or cholinomimetics mimic the action of acetylcholine to stimulate the parasympathetic nervous system. They are categorized into direct-acting and indirect-acting agents. The direct-acting cholinergic drugs induce the parasympathetic response by directly binding to the muscarinic or nicotine receptors. In comparison, the indirect-acting cholinergic drugs prevent acetylcholine hydrolysis, indirectly contributing to the extended parasympathetic response.
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Updated: May 13, 2026

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

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Does electron-correlation has any role in the quantitative structure-activity relationships?

Vikas1, Reenu, Chayawan

  • 1Quantum Chemistry Group, Department of Chemistry & Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh 160 014, India. qlabspu@pu.ac.in

Journal of Molecular Graphics & Modelling
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study reveals that incorporating electron-correlation energy into quantitative structure-activity relationship (QSAR) models significantly enhances their robustness and predictive power for mutagenic activity. Models using electron-correlation descriptors outperform traditional Hartree-Fock and density-functional theory methods.

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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

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Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Toxicology

Background:

  • Quantitative structure-activity relationships (QSAR) commonly utilize quantum-mechanical descriptors from Hartree-Fock (HF) and density-functional theory (DFT) methods.
  • The accuracy of these quantum-chemical methods critically depends on the precise calculation of electron-correlation energy.
  • Electron-correlation energy's role as a molecular descriptor in QSAR has not been extensively investigated.

Purpose of the Study:

  • To analyze the impact of electron-correlation energy as a molecular descriptor on QSAR models.
  • To evaluate the role of electron-correlation in QSAR models for the mutagenic activity of nitrated polycyclic aromatic hydrocarbons (nitro-PAHs).
  • To compare the performance of QSAR models incorporating electron-correlation with those using traditional HF, DFT, and semi-empirical methods (PM6, RM1).

Main Methods:

  • Development and validation of QSAR models using electron-correlation energy and its contribution to highest occupied and lowest unoccupied molecular orbital (HOMO/LUMO) energies as molecular descriptors.
  • External validation of QSAR models using concordance correlation coefficient and predictive squared correlation coefficients (QF1(2), QF2(2), QF3(2)).
  • Comparison of QSAR models based on electron-correlation descriptors with those derived from HF, DFT, PM6, and RM1 methods.

Main Results:

  • QSAR models incorporating electron-correlation contribution to descriptors demonstrate superior robustness and predictivity compared to models based solely on HF and DFT descriptors.
  • Models using HF and DFT descriptors showed less reliability than those based on semi-empirical methods (PM6, RM1), which exhibited comparable robustness and predictivity.
  • Explicit inclusion of electron-correlation contribution significantly improves the external predictivity of QSAR models, even those based on semi-empirical descriptors.

Conclusions:

  • Electron-correlation energy and its contribution to HOMO/LUMO energies are valuable molecular descriptors for enhancing QSAR model performance.
  • This study presents the first use of electron-correlation energy and its HOMO/LUMO contributions as molecular descriptors in QSAR.
  • Incorporating electron-correlation provides more robust and predictive QSAR models for chemical mutagenicity assessment.