QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives

Luco, Juan M.; Ferretti, F.H.

doi:10.1021/ci960487o

Cited by 152 publications

(130 citation statements)

References 34 publications

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“…The PLS method works well when there are several noisy and inter-correlated descriptors, and also allows multiple responses to be modelled simultaneously (Eriksson & Johansson, 1996). The usefulness of PLS in (Q)SAR studies, especially when the descriptors are highly correlated and numerous, has been proven by several researchers (Cramer, Bunce, Patterson, & Frank, 1988;Dunn, Wold, Edlund, Hellberg, & Gasteiger, 1984;Eriksson, Gottfries, Johansson, & Wold, 2004;Gu, et al, 2012;Luco, 1999;Luco & Ferretti, 1997). However, this method can only be used for the solution of linear regression problems.…”

Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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“…It includes methods such as multiple linear regression (MLR) and partial least square (PLS) [42][43][44]. In a MLR analysis the predictors in the regression equations should be independent variables of which the values are obtained directly from calculation or experiment.…”

Section: Discussionmentioning

confidence: 99%

QSAR studies of a number of triazole antifungal alcohols

Nowaczyk

Modzelewska-Banachiewicz

2010

Open Chemistry

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Abstract:The activity of fungicide agents containing a quinazolinone ring was described using the quantitative structure-activity relationship (QSAR) model by applying it to data taken from literature. The title compounds exhibit two important types of activity against certain fungal pathogens, i.e. activity against yeast and activity against filamentous fungi. A correlation between both antifungal activities (e.g. FA(yst) and FA(ff)) and physicochemical parameters such as the logarithm of the n-octanol/water partition coefficient (log P), the polarizability (P), the global minimum energy (TE), the energy difference between the frontier molecular orbital (DELH) and the molar refractivity (MR), was established using multiple linear regression. The molecular descriptors of the antifungal agents were obtained by quantum chemical calculations combined with molecular modeling calculations. Statistical analysis shows that the antifungal activity depends mainly on the calculated partition coefficients, log P, of the compounds. Bi-parametric models reveal that antifungal activity relates linearly to log P and P. © Versita Sp. z o.o.

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“…Thereafter, multiple model equations were generated using multiple linear regression (MLR), [17] partial least squares regression (PLSR) [18], and k-nearest neighbor (kNN) [19]. The model was considered to have a significant predictivity when the squared correlation coefficient (r 2 ) between descriptors and activity (pIC) was more than 0.6.…”

Section: Model Buildingmentioning

confidence: 99%

Application of Group-Based QSAR and Molecular Docking in the Design of Insulin-Like Growth Factor Antagonists

Abdullahi¹,

Abdualkader²,

Abdulsamat³

et al. 2015

Trop. J. Pharm Res

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Purpose: To identify the structural requirements for designing a lead key for insulin-like growth factor (IGF-1R) inhibition using group-based quantitative structure activity relationship (GQSAR Met 1126, Arg, 1128, Met 1052, GLU 1050, Met 1112, Leu 1051, Met 1049, Val 1033 at ATP binding sites of IGF-1R kinase domain. Conclusion:The generated models provide a site-specific insight into the structural requirements for IGF-1R inhibition which can be used to design and develop potent inhibitors.

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QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives

Cited by 152 publications

References 34 publications

(Q)SAR modelling of nanomaterial toxicity: A critical review

(Q)SAR modelling of nanomaterial toxicity: A critical review

QSAR studies of a number of triazole antifungal alcohols

Application of Group-Based QSAR and Molecular Docking in the Design of Insulin-Like Growth Factor Antagonists

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