QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES

The CORAL software (http:/www.insilico.eu/CORAL) has been examined as a tool of modeling of the angiotensin-converting enzyme-inhibitor activity of 54 tri-peptides. Three versions of the models were examined: (i) models based on the graph of atomic orbitals (GAO); (ii) models based on the simplified molecular input-line entry systems (SMILES); and (iii) hybrid models based on both GAO and SMILES. The hybrid models have provided the best prediction. The robustness of the approach has been checked with four random splits into training set and test set.     

QSAR as a random event: criteria of predictive potential for a chance model

The CORAL software (http://www.insilico.eu/coral) was suggested as a tool to build up quantitative structure–property/activity relationships (QSPRs/QSARs). This software is based on conception “a QSPR/QSAR model should be interpreted as a random event.” This is reflection of fact: different distributions into the training set (substances involved in modeling process) and the validation set (substances, which are not known at the moment of the modeling process) give models with significant dispersion in the statistical quality of the QSPR/QSAR. Results of experiments with the software and possible ways of further improvement of this software are discussed. The most attractive new ways to estimate predictive potential of the CORAL model seem to be the following ones: (i) index of ideality of correlation and (ii) correlation contradiction index. These can be also proposed as criteria of predictive potential for arbitrary QSPR/QSAR.

     

Semi-correlations as a tool to build up categorical (active/inactive) model of GABAA receptor modulator activity

The interpretation of mode of action for GABA_A receptor modulator activity is an important task of medicinal chemistry. The computational elucidation of the modulator activity is one of the ways to solve the above task. So-called semi-correlation is a tool for prediction of GABA_A receptor modulator activity. The semi-correlation is based on the Monte Carlo method. This approach is to build up categorical classification models into two classes: (i) active and (ii) inactive. The CORAL software (http://www.insilico.eu/coral) can be used to build up the semi-correlations. The statistical quality of models (for external validation sets) based on semi-correlation has the range of Matthews correlation coefficient (MCC) is 0.72–1.00 for 30 random splits of all available data (n?=?210) into the training and validation sets. In contrast to existing approaches, the predictive CORAL models give prediction using solely data on molecular architecture (represented by simplified molecular input-line entry system?=?SMILES) and available experimental data on endpoints. Suggested models for prediction of GABA_A receptor modulator activity are built up according to the OECD principles. Thus, the approach based on the semi-correlation can be a useful tool for studying of the GABA_A receptor modulators activity.

     

醛酮化合物色谱保留指数的集成全息定量构效关系模型

色谱保留指数(retention index,RI)是色谱分析中的重要参数,不同化合物在不同极性固定相上具有不同的保留行为.醛酮化合物种类众多,实验测定其RI值的时间和经济成本高.该论文采用集成建模(ensemble modeling)结合全息定量构效关系(HQSAR)方法研究了醛酮化合物在2种固定相(DB-210和H...     

The index of ideality of correlation: improvement of models for toxicity to algae

Toxicity to algae is important characteristic of substances from ecologic point of view. The CORAL software (http://www.insilico.eu/coral) gives possibility to build up model of toxicity to algae using data on the molecular architecture and experimental toxicity, without additional data on physicochemical and/or biochemical parameters. Considerable improvement of the model is observed in the case of using the index of ideality of correlation (IIC) in the role of additional criterion of predictive potential. The IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The best model calculated with use the IIC is characterized (the validation set) by n?=?50, r²?=?0.947, RMSE?=?0.401 whereas, model calculated without use the IIC is characterized by n?=?50, r²?=?0.805, and RMSE?=?0.539. The suggested models are built up in accordance to five OECD principles.

     

Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas)

CORrelation And Logic (CORAL) is a software that generates quantitative structure activity relationships (QSAR) for different endpoints. This study is dedicated to the QSAR analysis of acute toxicity in Fathead minnow (Pimephales promelas). Statistical quality for the external test set is a complex function of the split (into training and test subsets), the number of epochs of the Monte Carlo optimization, and the threshold that is a criterion for dividing the correlation weights into two classes rare (blocked) and not rare (active). Computational experiments with three random splits (data on 568 compounds) indicated that this approach can satisfactorily predict the desired endpoint (the negative decimal logarithm of the 50% lethal concentration, in mmol/L, pLC₅₀). The average correlation coefficients (r²) are 0.675 ± 0.0053, 0.824 ± 0.0242, 0.787 ± 0.0101 for subtraining, calibration, and test set, respectively. The average standard errors of estimation (s) are 0.837 ± 0.021, 0.555 ± 0.047, 0.606 ± 0.049 for subtraining, calibration, and test set, respectively. The CORAL software together with three random splits into subtraining, calibration, and test sets can be downloaded on the Internet ( http://www.insilico.eu/coral/ ). © 2012 Wiley Periodicals, Inc.     

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .     

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

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_(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. Copyright © 2009 John Wiley & Sons, Ltd.     

DFT‐based QSAR study and molecular design of AHMA derivatives as potent anticancer agents

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 (MR₂) of subsituent R₂ 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 (R²) and the “LOO” cross‐validation coefficient (q²) 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     

Binding conformations,QSAR, and molecular design of Alkene‐3‐quinolinecarbonitriles as Src inhibitors

A theoretical study on binding orientations and quantitative structure–activity relationship (QSAR) of a novel series of alkene‐3‐quinolinecarbonitriles acting as Src inhibitors has been carried out by using the docking study and three‐dimensional QSAR (3D‐QSAR) analyses. The appropriate binding orientations and conformations of these compounds interacting with Src kinase were revealed by the docking studies, and the established 3D‐QSAR models show significant statistical quality and satisfactory predictive ability, with high R² values and q² values: comparative molecular field analysis (CoMFA) model (q² = 0.748, R² = 0.972), comparative molecular similarity indices analysis (CoMSIA) model (q² = 0.731, R² = 0.987). The systemic external validation indicated that both CoMFA and CoMSIA models possessed high predictive powers with $ R{^2}_{\!\!\!\rm pred} $ values of 0.818 and 0.892, $ {r^2}_{\!\!\!\rm m} $ values of 0.879 and 0.886, $ {r^2}_{\!\!\!\rm m(LOO)} $ values of 0.874 and 0.874, $ r^2_{\rm m(overall)} $ values of 0.879 and 0.885, respectively. Several key structural features of the compounds responsible for inhibitory activity were discussed in detail. Based on these structural factors, eight new compounds with quite higher predicted Src‐inhibitory activities have been designed and presented. We hope these theoretical results can offer some valuable references for the pharmaceutical molecular design as well as the action mechanism analysis. © 2012 Wiley Periodicals, Inc.