Prediction of halocarbon toxicity from structure: a hierarchical QSAR approach

Gute, Brian D.; Balasubramanian, K.A.; Geiss, Kevin T.; Basak, Subhash C.

doi:10.1016/j.etap.2003.10.005

Cited by 14 publications

(5 citation statements)

References 19 publications

(5 reference statements)

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“…Basak et al formulated the hierarchical quantitative structure-activity relationship (HiQSAR) approach for the estimation of properties, biomedicinal activities, and toxicities of chemicals from computed descriptors. [6][7][8][9][10][11][12][13][14][15][16][17][18] The objective of this HiQSAR/ HiQSPR research has been twofold: description and prediction. The HiQSPR formalism uses progressively more complex indices in the development of models.…”

Section: Introductionmentioning

confidence: 99%

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Basak¹,

Mills²

2005

Arkivoc

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Vapor pressure is an important property which is an indicator of chemical volatility, along with transport, partitioning, fate and distribution of environmental pollutants. Various models have been developed for the prediction of vapor pressure of chemicals using physicochemical and calculated structural properties. We have used different classes of graph theoretic indices, e.g., topostructural indices, topochemical indices, geometrical (3D) indices and, quantum chemical descriptors, for the development of predictive models for vapor based on a structurally diverse set of 469 chemicals. Initially, a set of 379 molecular descriptors was calculated using the software POLLY, Triplet, Sybyl, MOPAC, and Molconn-Z. Comparatively, three linear regression methodologies were used to develop hierarchical QSAR (HiQSAR) models, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression. The results indicate that, in general, RR outperforms PCR and PLS, and that the easily calculated topological descriptors are sufficient for the prediction of vapor pressure based on this large, diverse set of chemicals.

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Section: Introductionmentioning

confidence: 99%

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Basak¹,

Mills²

2005

Arkivoc

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“…The addition of 3-D or QC descriptors after the use of TS+TC combination does not make any improvement in model quality. We previously observed this trend in different properties of other data sets [23][24][25][26][27][28][29][30]. At this age of "big data screening and analysis" [31], this is a good news because QSARs derived from the less expensive TS+ TC combination can be effective tools in the fast and effective screening of large chemical libraries.…”

Section: Discussionmentioning

confidence: 85%

Hierarchical quantitative structure-activity relationships (HiQSARs) for the prediction of physicochemical and toxicological properties of chemicals using computed molecular descriptors

Majumdar

Basak

2015

Proceedings of MOL2NET, International Conference on Multidisciplinary Sciences

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Attempts have been made to formulate quantitative structure=activity relationships (QSARs) for the prediction of property/ bioactivity of chemicals from their experimental test data as well as properties that can be computed directly from molecular structure without the input of any other experimental property. Because both in drug design and hazard assessment of chemical scenarios relevant experimental data for property/ bioactivity estimation are not available for the majority of candidate chemicals, QSARs based on computed molecular descriptors are emerging as methods of choice for property/ bioactivity estimation in many cases. Numerical graph invariants or topological indices, viz., topostructural (TS) indices, topochemical (TC) indices, as well as three-dimensional (3-D) descriptors, and quantum chemical (QC) indices have been used for QSAR formulation based on computed descriptors. In the 1990s, Basak et al formulated the concept of hierarchical quantitative structure=activity relationships (HiQSAR) in which TS, TC, 3-D, and QC descriptors were used in a graduated manner, the more computationally demanding descriptors being used only if the simpler ones did not give acceptable QSAR models. Our experience with a substantial number of HiQSARs for physical, pharmacological, and toxicological properties of different congeneric as well diverse sets chemicals indicate that the combinations of TS + TC descriptors are capable of giving good quality QSARs in most situations. The addition of 3-D or QC descriptors make marginal or no improvement in model quality after the use of TS+ TC descriptors. At this age of "big data screening and analysis" this is a good news SciForum Mol2Net, 2015, 1(Section B), pages 1-9, Proceedings 2 http://sciforum.net/conference/mol2net-1 because QSARs derived from the less expensive and practically useful TS+ TC combination can be effective tools in the screening of large chemical libraries.

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“…Previous studies have demonstrated that quantitative structure-activity/ property relationships (QSAR/QSPR) approach is successful in predicting activities, properties, and toxicities including mutagenicity (described as lnR) of aromatic and hetero-aromatic amines. [6][7][8][9][10][11][12] For example, the aryl hydrocarbon receptor binding affinity (described as pEC50) is well documented in the field of toxicology for organics. [13][14][15] Basak et al 16 proposed a hierarchical quantitative structure-activity relationship (HiQSAR) approach for the pEC50 prediction.…”

Section: Introductionmentioning

confidence: 99%

Structure-Property Relationship for the Pharmacological and Toxicological Activity of Heterocyclic Compounds

Zhu¹,

Wang²,

Jia³

et al. 2014

Acta Physico-Chimica Sinica

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Heterocyclic molecules play a crucial role in health care and in pharmaceutical drug design. A large number of drugs used in Western medical practice are heterocyclic molecules. In this study, a set of norm indexes of the extended distance matrix are proposed. From these a stable and accurate structureproperty relationship model was developed for the prediction of the aryl hydrocarbon receptor binding affinity (pEC50) of dibenzofurans and the mutagenic potency (lnR) of aromatic and heteroaromatic amines. Our results indicate that the new model, based on these norm indexes, provides very satisfactory results, and that the average absolute differences for pEC50 prediction and lnR prediction were 0.403 and 0.702 with r 2 (square correlation coefficient) values of 0.876 and 0.779, respectively. A comparison of these results with other methods demonstrates that our method, based only on the same mathematical model, performed better in terms of both accuracy and stability.

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Prediction of halocarbon toxicity from structure: a hierarchical QSAR approach

Cited by 14 publications

References 19 publications

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Hierarchical quantitative structure-activity relationships (HiQSARs) for the prediction of physicochemical and toxicological properties of chemicals using computed molecular descriptors

Structure-Property Relationship for the Pharmacological and Toxicological Activity of Heterocyclic Compounds

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