Structure-Based Design, Parallel Synthesis, Structure−Activity Relationship, and Molecular Modeling Studies of Thiocarbamates, New Potent Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitor Isosteres of Phenethylthiazolylthiourea Derivatives

Ranise, Angelo; Spallarossa, Andrea; Cesarini, Sara; Bondavalli, Francesco; Schenone, Silvia; Bruno, Olga; Menozzi, Giulia; Fossa, Paola; Mosti, Luisa; Colla, Massimiliano La; Sanna, Giuseppina; Murreddu, Marta; Collu, Gabriella; Busonera, Bernardetta; Marongiu, M. E.; Pani, Alessandra; Colla, Paolo La; Loddo, Roberta

doi:10.1021/jm049252r

Cited by 44 publications

(32 citation statements)

References 64 publications

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“…The cytoprotection data reported by Ranise et al [25] have been used as the model dataset for the present study: the cytoprotection data [EC 50 (50% effective concentration in mM)] of substituted thiocarbamates (Table 1) have been converted to the logarithmic scale (pEC 50 or À logEC 50 where EC 50 is in mM) and then used for subsequent QSAR analyses as the response variable. There are four regions of structural variations in the compounds: one is the R position of the phenyl ring (showing diverse substitution pattern) and the remaining are R 1 , R 2 , and B 1/2 positions (showing limited substitution pattern) ( Table 1).…”

Section: The Dataset and Descriptorsmentioning

confidence: 99%

“…Furthermore, R 2 pred may not be the only optimum criterion for selection of the variables; other factors like r 2 (squared correlation coefficient between observed and predicted values), r 2 0 (squared correlation coefficient between observed and predicted values with intercept value set to zero), and Root Mean Square Error of Prediction (RMSEP) [13] may be considered and variables should be selected based upon a suitable balance among all statistical parameters having values within their acceptable range. The present report tries to explore the optimum variable selection strategy in case of PLS regression using a model dataset of anti-HIV thiocarbamates [25]. The objective of this report is neither formulating new QSAR models for anti-HIV thiocarbamates nor com-paring the present models with the published ones.…”

Section: Introductionmentioning

confidence: 99%

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On Some Aspects of Variable Selection for Partial Least Squares Regression Models

2008

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This paper tries to explore the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data. The compounds of the dataset were classified using K-means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters. For a particular training set, PLS models were developed with a number of components optimized by leave-one-out Q 2 and then the developed models were validated (externally) using the test set compounds. For each set, PLS model was initially constructed using all descriptors (variables). The variables having least standardized values of regression coefficients were deleted and the next model was developed with a reduced set of variables. These steps were performed several times until further reduction in number of variables did not improve Q 2 value. In each case, statistical parameters like predictive R 2 (R 2 pred ), squared correlation coefficient between observed and predicted values with (r 2 ) and without (r 2 0 ) intercept and Root Mean Square Error of Prediction (RMSEP) were calculated from the test set compounds. In case of all ten sets, Q 2 values steadily increase on deletion of variables while R 2 pred values do not show any specific trend. In no case, the highest Q 2 and highest R 2 pred appear in the same trial, i.e., with the same combinations of variables. This suggests that from the viewpoint of external predictability, choice of variables for PLS based on Q 2 value may not be optimum. Moreover, a clear separation of r 2 and r 0 2 curves in some sets suggests that such models may not be truly predictive in spite of acceptable R 2 pred values. Another observation is that coefficient of determination R 2 for the training set is more immune to changes on deletion of variables than the validation parameters like Q 2 and R 2 pred . Finally, a new parameter r m 2 has been suggested to indicate external predictability of QSAR models.

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Section: The Dataset and Descriptorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

2008

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“…The cytoprotection data of anti-HIV thiocarbamates (n ¼ 62) reported by Ranise et al [58] have been used as the model data set: the cytoprotection data (EC 50 (mM)) of substituted thiocarbamates (Table I) have been converted to the logarithmic scale (pEC 50 (mM)) and then used for subsequent QSAR analyses as the response variable. There are four regions of structural variations in the compounds: one is the R position on the phenyl ring (showing diverse substitution pattern), and the remaining are R 1 , R 2 and B 1/2 positions (showing limited substitution pattern) (Table I).…”

Section: The Data Setmentioning

confidence: 99%

On further application of r as a metric for validation of QSAR models

Mitra

Roy

Kar

et al. 2009

Journal of Chemometrics

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Validation is a crucial aspect for quantitative structure-activity relationship (QSAR) model development. External validation is considered, in general, as the most conclusive proof of predictive capacity of a QSAR model. In the absence of truly external data set, external validation is usually performed on test set compounds, which are members of the original data set but not used in model development exercise. In the case of small data sets, QSAR researchers experience problem in model development due to the fact that the developed models may be less reliable on account of the small number of training set compounds and such models may also show poor external predictability because the models may not have captured all necessary features required for the particular structure-activity relationships. The present paper attempts to show that 'true r 2 m (LOO) ' statistic calculated based on the model derived from the undivided data set with application of variable selection strategy at each cycle of leave-one-out (LOO) validation may reflect external validation characteristics of the developed model thus obviating the requirement of splitting of the data set into training and test sets. This approach may be helpful in the case of small data sets as it uses all available data for model development and validation thus making the resulting model more reliable.

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“…The cytoprotection data reported by Ranise et al [20] have been used as the model dataset for the present QSAR study: the cytoprotection data [EC 50 (mM)] of substituted thiocarbamates (Table 1) (compound concentrations required to achieve 50% protection of MT-4 cells from HIV-1 induced cytopathogenecity) have been converted to the logarithmic scale [pEC 50 (mM)] and then used for subsequent QSAR analyses as the response variable. There are four regions of structural variations in the compounds: one is the R position of the phenyl ring (showing diverse substitution pattern), and the remaining are R 1 , R 2 and B 1/2 positions (showing limited substitution pattern) (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…In continuation of such efforts, the present paper deals with QSAR modeling of cytoprotection activity data of substituted thiocarbamates [20].…”

Section: Introductionmentioning

confidence: 99%

Comparative Classical QSAR Modeling of Anti‐HIV Thiocarbamates

Leonard

Roy

2007

QSAR Comb. Sci.

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The present Quantitative Structure -Activity Relationships (QSAR) study attempts to explore the structural and physicochemical requirements of O-[2-(phthalimido)ethyl]-substituted-phenylthiocarbamates for cytoprotection data from MT-4-based assays using a Linear Free Energy Related (LFER) model of Hansch. QSAR models have been developed using electronic (Hammett s), hydrophobicity (p) and steric (molar refractivity and STERIMOL L, B 1 , and B 5 ) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Statistical techniques like stepwise regression, Multiple Linear Regression (MLR) with Factor Analysis (FA) as the data preprocessing step (FA-MLR), Partial Least Squares (PLS), Principal Component Regression (PCR), Multiple Linear Regression with Genetic Function Approximation (GFA-MLR) and Genetic Partial Least Squares (G/PLS) model were applied to identify the structural and physicochemical requirements for the cytoprotection activity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from these techniques were of acceptable statistical qualities (explained variance ranging from 57.7 to 75.9%, while predicted variance ranging from 53.2 to 68.5%), while the best model came from the PLS technique. The PLS equation with variables selected based on standardized regression coefficients show explained and predicted variances of 75.9 and 68.1%, respectively, and the G/PLS explained and predicted variances of 75.1 and 68.5%, respectively, of the cytoprotection activity data. Both stepwise regression and GFA derived models show high intercorrelation among predictor variables used in the equations, while the FA-MLR derived model has comparatively lower statistical quality. The best models were also subjected to leave-25%-out crossvalidation. The results of the present QSAR study are in agreement with the observations of the previously reported docking experiment of thiocarbamate derivatives.

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Structure-Based Design, Parallel Synthesis, Structure−Activity Relationship, and Molecular Modeling Studies of Thiocarbamates, New Potent Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitor Isosteres of Phenethylthiazolylthiourea Derivatives

Cited by 44 publications

References 64 publications

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

On further application of r as a metric for validation of QSAR models

Comparative Classical QSAR Modeling of Anti‐HIV Thiocarbamates

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