Quantitative Structure–Retention Relationships of Polychlorinated Biphenyls (PCBs) Gas Chromatographic Retention Times: A Quantum Similarity Approach

Marrugo, Juan J.; Drosos, Juan Carlos; Gueto-Tettay, Carlos; Anaya-Gil, Jorge; Rincón, Luís; Vivas‐Reyes, Ricardo

doi:10.1007/s10337-013-2492-9

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The Carbó index and the alignment were performed in the authors homemade software based on previously reported works. [28] Once the molecules are superposed, the quantum similarity indexes for the series are computed using the Topo-Geometrical Superposition Algorithm (TGSA) with the Coulomblike (C) and Overlap-like (O) weighting operators. The promolecular Atomic Shell Approximations (ASA) based on 3-21G(B1), 6-31G(B2) and Huzinaga (B3) basis sets assisted the computation of density functions.…”

Section: Similarity Calculationsmentioning

confidence: 99%

“…The Carbó index and the alignment were performed in the authors homemade software based on previously reported works [28] . Once the molecules are superposed, the quantum similarity indexes for the series are computed using the Topo‐Geometrical Superposition Algorithm (TGSA) with the Coulomb‐like ( C ) and Overlap‐like ( O ) weighting operators.…”

Section: Computational Detailsmentioning

confidence: 99%

“…A conformational search with RDKit [28] found the molecular conformations of the molecule with higher similarity to THC. The geometries obtained by this procedure were called ‘S’ dataset.…”

Section: Computational Detailsmentioning

confidence: 99%

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QSAR Modelling of Biological Activity in Cannabinoids with Quantum Similarity Combinations of Charge Fitting Schemes and 3D‐QSAR

Navarro-Acosta

Coba‐Jiménez

Pérez-Gamboa

et al. 2023

Chemistry & Biodiversity

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Quantitative structureÀ activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi.nlm.nih.gov/] database provided the geometries, binding affinities(K i ) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD 50 ) to breast cancer cells. An innovative quantum similarity approach combining (self)-similarity indexes calculated with different charge-fitting schemes under the Topo-Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R 2 ) and leave-oneout cross-validation[Q 2 (LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD 50 : R 2 = 0.9666 and Q 2 (LOO) = 0.9312; pK i (CB1): R 2 = 1.0000 and Q 2 (LOO) = 0.9727, and pK i (CB2): R 2 = 0.9996 and Q 2 (LOO) = 0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similaritybased descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D-QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand-based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.

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Section: Similarity Calculationsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%