Traditional versus WHIM molecular descriptors in QSAR approaches applied to fish toxicity studies
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Abstract A quantitative structure–activity relationship (QSAR) of 3‐(9‐acridinylamino)‐5‐hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy ( E NL ) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges ( Q FR ) of the first atom of the substituent R , as well as the steric parameter ( MR 2 ) of subsituent R 2 are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by “leave‐one‐out” (LOO) cross‐validation coefficient ( q ) was suggested and successfully used. The fitting correlation coefficient ( R 2 ) and the “LOO” cross‐validation coefficient ( q 2 ) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007
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Caffeic Acid Phenethyl Ester (CAPE) compounds are potentially colorectal anticancer drugs. QSAR (Quantitative Structure-Activity Relationship) research on the CAPE compound class has been carried out, but the model in the previous study did not meet the goodness of fit criteria. The development of the CAPE compound QSAR model as a colorectal anticancer was carried out to obtain a model that meets the goodness of fit criteria and is valid. Eighteen CAPE compounds were used to build the QSAR model using the Multiple Linear Regression (MLR) technique. The descriptor selection was carried out using the backward elimination method and the validation test using the internal leave one out (LOO) cross-validation. The results showed that the QSAR model with four descriptors, namely MDEC22, MDEC23, JGI1, and molecular weight (BM), met the goodness of fit and Q2(LOO) criteria. The development of the QSAR model by adding the LogP descriptor resulted in the QSAR 5 descriptor model with higher goodness of fit level than the QSAR model without the LogP descriptor. Both of these QSAR models have the potential to be used as predictors in the development of a new class of CAPE compounds as anticancer agents against HT-29 cells.
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In the present study, we explored a series of molecules with anticancer activity, so that qualitative and quantitative studies of the structure-activity relationship (SAR/QSAR) were performed on seventeen theophylline derivatives. These are inhibitors of ALDH1A1. The present study shows the importance of quantum chemical descriptors, constitutional descriptors and hydrophobicity to develop a better QSAR model, whose studied descriptors are LogP, MW, Pol, MR, S, V, HE, DM, EHOMO and ELUMO. A multiple linear regression (MLR) and artificial neural networks (ANN) procedure was used to design the relationships between molecular descriptors and the inhibition of ALDH1A1 by theophylline derivatives. The validation and good quality of the QSAR model are confirmed by a strong correlation between experimental and predicted activity.
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Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R²) > 0.7, explained variance in leave-one-out prediction (Q²LOO) and in leave-many-out prediction (Q²LMO) > 0.6, variance explained in external prediction (Q²F1, Q²F2, and Q²F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
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Salicylamide
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The present work has focused on the application of the inverse-QSAR/QSPR problem for generating new structures of pesticides; this is in view of its extremely important and widespread use in several areas, particularly the agricultural field. For this reason, we implemented a methodology containing nine detailed successive steps that include a quantitative structure–activity/property relationship (QSAR/QSPR) study performed to develop a model that relate s the structures of 190 pesticides compounds to their n-octanol–water partition coefficients ( logk ow ). We used the unique atomic signatures which represent the structures and acts as independent variables while the property ( logk ow ) as the dependent variable. The model was constructed using 130 molecules as training set, and predictive ability tested using 60 compounds. Modeling of logk ow of these compounds as a function of the signatures descriptors was established by multiple linear regression (MLR) using (LOO) cross-validation. As a result, a QSAR/QSPR equation with 14 atomic signatures was hereby obtained with a R 2 =0.659273, Q 2 =0.65617 and RMSE training = 0.930192, s=1.37297 for the training set and in leave-one-out (LOO) cross-validation experiment set value, q 2 =0.605676, RMSE LOO = 1.0936 respectively. In addition to all of the above, new structures have been generated for a range of pesticides that can be included as future search topics.
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Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.
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Benzothiazole
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