A QSPR Study of Association Constants of Macrocycles toward Sodium Cation (original) (raw)
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Towards computational prediction of Biopharmaceutics Classification System: a QSPR approach
Proceedings of MOL2NET, International Conference on Multidisciplinary Sciences, 2015
Today classification of drug candidates on the Biopharmaceutics Classification System (BCS) has become an important issue in pharmaceutical researches. In this work, we provide a potential in silico approach to predict this system using two separately classification models of Dose number and Caco-2 cell permeability. 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the solubility and permeability properties. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7% in training and 92.3% in test set. Likewise, for the permeability model (VoteP), accuracy was 85.3% in training and 96.9% in test set. A combination of VoteS and VoteP appropriately predicts the BCS class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts the BCS allocations of 57 drugs appeared in the WHO Model List of Essential Medicines with 87.5% of accuracy. A simulation of a biopharmaceutical screening assay has been proved in a large data set of 37,377 compounds in different drug development phases (1, 2, 3 and launched), and NMEs. Distributions of BCS forecasts illustrate the current status in drug discovery and development. It is anticipated that developed QSPR models could offer the best estimation of BCS for NMEs in early stages of drug discovery.
Journal of Enzyme Inhibition and Medicinal Chemistry, 2008
Quantitative structure-activity relationship (QSAR) studies have been carried out on indolyl aryl sulfones, a class of novel HIV-1 non-nucleoside reverse transcriptase inhibitors, using physicochemical, topological and structural parameters along with appropriate indicator variables. The statistical tools used were linear methods (e.g., stepwise regression analysis, partial least squares (PLS), factor analysis followed by multiple regression (FA-MLR), genetic function approximation combined with multiple linear regression (GFA-MLR) and GFA followed by PLS or G/PLS and nonlinear method (artificial neural network or ANN). In case of physicochemical parameters, GFA-MLR generated the best Equation (n ¼ 97, R 2 ¼ 0.862, Q 2 ¼ 0.821). Using topological parameters, the best Equation (based on leave-one-out Q 2) was obtained with stepwise regression technique (n ¼ 97, R 2 ¼ 0.867, Q 2 ¼ 0.811). When topological and physicochemical parameters were used in combination, statistical quality increased to a great extent (n ¼ 97, R 2 ¼ 0.891, Q 2 ¼ 0.849 from stepwise regression). Furthermore, the whole dataset had been divided into test (25% of whole dataset) and training (remaining 75%) sets. Models were developed based on the training set and predictive potential of such models was checked from the test set. The selection of the training set was based on K-means clustering of the standardized descriptors (topological and physicochemical). In this case also the best results were obtained with stepwise regression (n ¼ 72, R 2 ¼ 0.906, Q 2 ¼ 0.853) but external predictive capacity of this model (R 2 pred ¼ 0:738) was inferior to the model developed from GFA-MLR technique (R 2 ¼ 0.883, Q 2 ¼ 0.823, R 2 pred ¼ 0:760). However, the squared regression coefficient between observed activity and predicted activity values of the test set compounds for the best linear model, i.e., GFA-MLR (r 2 ¼ 0.736) was lower in comparison to the best nonlinear model developed using artificial neural network (r 2 ¼ 0.781). Thus, based on external validation, the ANN models were superior to the linear models. The predictive potential of the best linear Equation (stepwise regression model) was superior to that of the previously published CoMFA (Q 2 ¼ 0.81, SDEP Test ¼ 0.89) on the same data set (Ragno R. et al., J Med Chem 2006, 49, 3172-3184). Furthermore, the physicochemical parameter based models also supported the previous observations based on docking (Ragno R. et al.,
Towards Computational Prediction of Biopharmaceutics Classification System: A QSPR Approach *
2016
System (BCS) has become an important issue in pharmaceutical researches. In this work, we provide a potential in silico approach to predict this system using two separately classification models of Dose number and Caco-2 cell permeability. 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the solubility and permeability properties. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7 % in training and 92.3 % in test set. Likewise, for the permeability model (VoteP), accuracy was 85.3 % in training and 96.9 % in test set. A combination of VoteS and VoteP appropriately predicts the BCS class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts the BCS allocations of 57 drugs appeared in the WHO Model List of Essential Medicines with 87.5 % of accuracy. A simulation of a biopharmaceutical screening assay has been proved in a large data ...
2018
Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology,<br> Jadavpur University, Kolkata-700 032, India<br> E-mail: kunal.roy@jadavpuruniversity.in Fax: 91-33-28371078<br> Manuscript received online 02 November 2018, accepted 26 November 2018 In this presentation, different quantitative structure-activity relationship (QSAR) modeling approaches and their use in drug<br> design and ecotoxicological modeling are briefly stated. The aspects of feature selection, modeling algorithms and validation<br> strategies are mentioned at an elementary level. Different novel strategies for improving statistical quality and predictive ability<br> of QSAR models are also cursorily presented. Finally, four useful tools for QSAR model validation as developed by the Drug<br> Theoretics and Cheminformatics (DTC) Laboratory of Jadavpur University are discussed. These tools are available for public<br> use via http://t...
A Novel Method of Validation of the QSPRs Based on Molecular Similarity
Revista de Chimie
For practical drug design purposes, the proposed method should be applied only if the analyzed database includes a prediction set (a group of a new molecules, not yet synthesized, with unknown values of the dependent property). The characteristics of the proposed method are: a) elimination of some molecules from the initial calibration and prediction sets, according to the result of a specific molecular similarity procedure b) the validation set is identified based on the results of similarity calculations and includes the molecules of the new calibration set most similar to the molecules of the new prediction set c) inclusion of the validation set in the new calibration set, used for model building d) it uses an original mathematical formula as validation function e) the most suitable equation for the description of the molecules in the new calibration set is different from the validated equation; these two equations are identified within the group of the best 1000 QSPRs f) the val...
Doklady Chemistry, 2009
Nowadays, the development of methodology of constructing quantitative structure-activity and structure-property relationship (QSAR/QSPR) models aimed at improving the descriptor representation of chemical compounds and at applying increasingly sophisticated methods of analysis has achieved the saturation level when the available methods make it possible to extract from databases almost all information useful for prediction. As stated in [1], in most cases, the predictive power of models constructed with the use of "fairly good" sets of descriptors and fairly good methods of data processing depends only slightly on both the descriptor set and the method used and is nearly completely determined by the database used for constructing a model. Thus, further improvement of the descriptor representation of chemical compounds and the introduction of new machine-learning methods will lead only to little progress, whereas radically new ideas are required for the actual breakthrough in this direction to overcome the limitations caused by a lack of useful information in chemical databases.
Molecules, 2011
The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC 50 of CXCR2 receptors for which no experimental data is available. 2011, 16 1929
Journal of Inclusion Phenomena and Macrocyclic Chemistry
A quantitative structure–property relationship (QSPR) study is performed to develop a model, relating to Na+ complex stability constant (log K) and the structure of 74 derivatives of 1,4,7,10,13-pentaoxacyclo-pentadecane ethers (15C5). Stepwise Multiple Linear Regression (SMLR) and Artificial Neural Network (ANN) methods have been exploited as linear and nonlinear methods, respectively to build the QSPR model. MOPAC software embedded in ChemOffice 2004 package was used for the minimizing energy using semi-empirical AM1 method. The optimum structures have been applied to generate more than 50 descriptors using available servers in ChemOffice 2004. The five most important constitutional, steric, electronic, thermodynamic and molecular descriptors were selected using the common preselection method combined by SMLR method. SMLR and ANN models were constructed based on the five selected descriptors. Both proposed models efficiently predict log K of 15C5 complexes. However, the results of ANN were more effective respect to SMLR model. This phenomenon reveals that log K of 15C5 complexes have a deviation from linear behavior related to the selected descriptors.
Journal of Computational Chemistry, 2021
The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions. K E Y W O R D S alternatives to animal testing, in silico predictions, QSAR, QSARINS, virtual screening 1 | INTRODUCTION Chemical pollution has great impact on human and environmental health and the development of strategies to guarantee a more sustainable use of chemicals is a main challenge for chemical regulations worldwide. 1-3 The need to properly address and manage chemical risks as well as to track and substitute potentially hazardous chemicals with less dangerous ones, has in the last decade pushed toward a faster development and integration of in vitro and in silico strategies within regulations. The effort spent in traditional and regulatory science to facilitate the application of in silico tools, making them more transparent, easy to apply, and efficient, is major. 3 In silico approaches, such as models based on Quantitative Structure Activity Relationships (QSAR), are used to predict many different properties and activities of regulatory interest and for different chemical categories. 4-17 These models are useful to fill data gaps, for virtual screenings, and/or for the identification of safer alternatives to unsafe pollutants. Furthermore, the availability of multiple models, which can be combined to generate consensus predictions, helps to reduce the uncertainty associated with the prediction of a single property/activity, they can cross-validate in silico predictions and experiments, and support decision making processes. 4-13 QSARINS-Chem 17 was proposed in 2014 as an additional module embedded in the software QSARINS 18 to provide a database to store models and a tool to facilitate their application. The QSARINS-Chem module included a database of chemical structures (with 3D