Multivariate Analysis of the Input and Output Data in the Fugacity Model Level I

Devillers, James; Thioulouse, Jean; Domine, D.; Chastrette, M.; Karcher, W.

doi:10.1007/978-94-011-3198-8_9

Cited by 6 publications

(5 citation statements)

References 225 publications

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“…From this we conclude that no single variable model is capable of modeling the activity and that the refereed descriptors can be combined to obtain a statistically significant multiparametric model for modeling the activity. Also, that models containing two or more topological (except J) indices may suffer from defect due to correlation [22][23][24] . However, such cases are nicely dealt with Randic 30 and we will use his recommendations to analyze such cases.…”

Section: Resultsmentioning

confidence: 99%

“…For obtaining appropriate QSAR model we have used maximum R 2 method and followed stepwise regression analysis. [22][23][24] The predictive ability of the model is discussed on the basis of predictive correlation coefficient. We have separated a set of potential inhibitors of E.Coli and finally we have aimed at the most appropriate model using molecular modeling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QSAR study on inhibition of E. Coli by sulfonamides

Thakur¹,

Thakur²,

Khadikar³

2006

Arkivoc

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The paper describes a QSAR study on inhibition of E. Coli by sulfonamides using distance-based topological indices. The sulfonamides discussed consists of 39 derivatives with substitution at 2-, 3-and 4-positions as well as having some di-substitution. Application of multiple linear regression analysis indicated that combination of distance-based topological indices with ad hoc molecular descriptors and indicator parameters yielded statistically significant model for modeling inhibitory activity (log1/C) of E. Coli. Predictive potential of the model is obtained by cross-validation parameters as well as by using a variety of other statistics. The results have indicated that there is no involvement of a positive hydrophobic term (logP) in the inhibition process, suggesting that the binding of the sulfonamides to the active center does not depend on hydrophobic interactions. Final selection of potential sulfonamide is made by molecular modeling.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QSAR study on inhibition of E. Coli by sulfonamides

Thakur¹,

Thakur²,

Khadikar³

2006

Arkivoc

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“…The 1-octanol/water partition coefficient (log P) of the organic molecules is undoubtedly the most important physicochemical property for explaining numerous of their pharmacological and toxicological effects as well as their behavior in the environment. This explains why log P is a long-established molecular descriptor in SAR and QSAR modeling, leading to thousands of published models in which it is used alone or with other molecular descriptors [17,18,53]. The number of experimental log P values available being very small compared to the universe of molecular structures that exist and can be drawn and/or synthetized, various methods have been proposed to reduce the time and cost of obtaining this physicochemical parameter.…”

Section: Discussionmentioning

confidence: 99%

“…The second one consists in the computation of (Quantitative) Structure-Activity Relationship ((Q)SAR) models [17]. Molecules with their experimental repellent activity are encoded by molecular descriptors and then a linear or nonlinear statistical method [18] is used for establishing formal relationships between the structure of the molecules and their repellent activity. The choice of the statistical approach mainly depends on the complexity of the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results

Devillers,

Devillers

2024

Applied Sciences

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Repellents play a fundamental role in vector control and prevention to keep mosquitoes away from humans. Available in limited numbers, it is absolutely necessary to find new repellents for preventing problems of resistance. QSAR (Quantitative Structure–Activity Relationship) methods are particularly suited for designing molecules with potential repellent activity. These models require that the molecules be described by physicochemical properties, topological indices, and/or structural indicators. In the former situation, QSPR (Quantitative Structure–Property Relationship) models are used for calculating physicochemical descriptors. Use of different QSPR models for the same property can lead to different values for the same molecule. In this context, the influence of the 1-octanol/water partition coefficient (log P) calculated according to two different methodologies was statistically evaluated in the modeling of 2171 molecules for which their skin repellent activity against Aedes aegypti was available. The two series of supervised artificial neural networks differed only by their input neuron coding for log P. Although both categories of classification models led to overall good statistics, we clearly showed that differences in log P values calculated for a molecule could result in very different prediction results. This was especially true for repellents. The practical implication of these differences was discussed.

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“…We recommend that the modeler strive to express the findings, not just as single numbers, but as means with expected variances. These variances can be determined from a sensitivity analysis on the input data, as described by Devillers et al [28] or Boesten [29]. The way in which variances in input parameters propagate through the system of equations is often not obvious, so no general rules apply and the most influential parameter(s) cannot be identified in advance.…”

Section: Stage 4 Regional or Far-field Evaluationmentioning

confidence: 99%

Assessing the fate of new and existing chemicals: A five‐stage process

Mackay

Guardo

Paterson

et al. 1996

Enviro Toxic and Chemistry

114

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Abstract-A five-stage process is described for obtaining an understanding of the fate of a substance after discharge to the environment and for predicting the concentrations to which organisms in various environmental media will be exposed. These five stages are: classifying the substance as to its chemical type and collecting the appropriate physical, chemical, and reactivity data based on this classification; obtaining information on the substance's past, present, and/or proposed production, use, and discharges and, if appropriate, background concentrations; conducting an evaluative fate assessment with the objective of determining the general features of the substance's behavior; conducting a regional or far-field fate evaluation using regional climatic and geographic conditions, to determine the role of environmental conditions on the substance's behavior and estimate average regional concentrations; and conducting one or more local or near-field evaluations on points of entry and other potentially impacted sites to predict the exposure concentration. With this information on fate, risk can be assessed by comparing predicted environmental concentrations with no-effect concentrations. This paper contains a detailed discussion of stage 1 (classification into one of five categories) and a discussion of how multimedia models can be used to conduct the evaluative (stage 3), regional (stage 4), and near-field (stage 5) assessments.

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Multivariate Analysis of the Input and Output Data in the Fugacity Model Level I

Cited by 6 publications

References 225 publications

QSAR study on inhibition of E. Coli by sulfonamides

QSAR study on inhibition of E. Coli by sulfonamides

Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results

Assessing the fate of new and existing chemicals: A five‐stage process

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