QNA-based ‘Star Track’ QSAR approach

In the existing quantitative structure–activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the ‘Star Track’ methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.

[1]  A. Varnek,et al.  Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening , 2009 .

[2]  R. Bader,et al.  Forces in molecules. , 2007, Faraday discussions.

[3]  K. Roy,et al.  QSAR of estrogen receptor modulators: exploring selectivity requirements for ER(alpha) versus ER(beta) binding of tetrahydroisoquinoline derivatives using E-state and physicochemical parameters. , 2005, Bioorganic & medicinal chemistry letters.

[4]  N. Dessalew,et al.  3D-QSAR and molecular docking study on bisarylmaleimide series as glycogen synthase kinase 3, cyclin dependent kinase 2 and cyclin dependent kinase 4 inhibitors: an insight into the criteria for selectivity. , 2007, European journal of medicinal chemistry.

[5]  Vladimir Poroikov,et al.  Probabilistic Approaches in Activity Prediction , 2008 .

[6]  S. Parasuraman.  Prediction of activity spectra for substances , 2011, Journal of pharmacology & pharmacotherapeutics.

[7]  J C Madden,et al.  An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis. , 2008, Chemosphere.

[8]  Alexander Tropsha,et al.  Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..

[9]  Judith C. Madden,et al.  Consensus QSAR Models: Do the Benefits Outweigh the Complexity? , 2007, J. Chem. Inf. Model..

[10]  Kunal Roy,et al.  QSTR with Extended Topochemical Atom Indices. 4. Modeling of the Acute Toxicity of Phenylsulfonyl Carboxylates to Vibrio fischeri using Principal Component Factor Analysis and Principal Component Regression Analysis , 2004 .

[11]  Alexandre Varnek,et al.  Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures , 2005, J. Comput. Aided Mol. Des..

[12]  Hongmao Sun,et al.  A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption , 2004, J. Chem. Inf. Model..

[13]  Alexander Tropsha,et al.  k Nearest Neighbors QSAR Modeling as a Variational Problem: Theory and Applications , 2005, J. Chem. Inf. Model..

[14]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[15]  Vladimir Poroikov,et al.  Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors , 1999, J. Chem. Inf. Comput. Sci..

[16]  Haralambos Sarimveis,et al.  A Novel QSAR Model for Evaluating and Predicting the Inhibition Activity of Dipeptidyl Aspartyl Fluoromethylketones , 2006 .

[17]  Lisa Peltason,et al.  Molecular Similarity Analysis in Virtual Screening , 2009 .

[18]  COMFA of the acute toxicity of phenylsulfonyl carboxylates to Vibrio fischeri , 2003, SAR and QSAR in environmental research.

[19]  Tudor I. Oprea,et al.  An automated PLS search for biologically relevant QSAR descriptors , 2004, J. Comput. Aided Mol. Des..

[20]  V V Poroikov,et al.  A new approach to QSAR modelling of acute toxicity , 2007, SAR and QSAR in environmental research.

[21]  Andreas Bender,et al.  Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier , 2004, J. Chem. Inf. Model..

[22]  Mark T D Cronin,et al.  Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis. , 2002, Chemosphere.

[23]  Anton J. Hopfinger,et al.  4D-Fingerprints, Universal QSAR and QSPR Descriptors , 2004, J. Chem. Inf. Model..

[24]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[25]  Igor V. Tetko,et al.  Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection , 2008, J. Chem. Inf. Model..

[26]  M. López-Rodríguez,et al.  Synthesis and structure-activity relationships of a new model of arylpiperazines. 2. Three-dimensional quantitative structure-activity relationships of hydantoin-phenylpiperazine derivatives with affinity for 5-HT1A and alpha 1 receptors. A comparison of CoMFA models. , 1997, Journal of medicinal chemistry.

[27]  Igor I. Baskin,et al.  Chapter 1:Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening , 2008 .

[28]  Robert C. Glen,et al.  Novel Methods for the Prediction of logP, pKa, and logD , 2002, J. Chem. Inf. Comput. Sci..

[29]  V. V. Poroikov,et al.  The Method of Self-Consistent Regression for the Quantitative Analysis of Relationships Between Structure and Properties of Chemicals , 2004, Pharmaceutical Chemistry Journal.

[30]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[31]  H. Fang,et al.  Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor , 2003, SAR and QSAR in environmental research.

[32]  Vladimir Poroikov,et al.  Chapter 6:Probabilistic Approaches in Activity Prediction , 2008 .

[33]  Vladimir Poroikov,et al.  Computer‐Aided Estimation of Synthetic CompoundsSimilarity with Endogenous Bioregulations , 1998 .

[34]  Mark T. D. Cronin,et al.  QSAR Analysis of the Toxicity of Aromatic Compounds to Chlorella vulgaris in a Novel Short-Term Assay , 2004, J. Chem. Inf. Model..

[35]  Weida Tong,et al.  Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics , 2008, J. Chem. Inf. Model..

[36]  Paul Greengard,et al.  Pharmacological inhibitors of cyclin-dependent kinases. , 2002, Trends in pharmacological sciences.

[37]  Alexander Golbraikh,et al.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection , 2004, Molecular Diversity.

[38]  Kunal Roy,et al.  QSTR with extended topochemical atom indices. Part 5: Modeling of the acute toxicity of phenylsulfonyl carboxylates to Vibrio fischeri using genetic function approximation. , 2005, Bioorganic & medicinal chemistry.

[39]  Antti Poso,et al.  A comparative molecular field analysis of cytochrome P450 2A5 and 2A6 inhibitors , 2001, J. Comput. Aided Mol. Des..

[40]  Frank R. Burden,et al.  Toward Novel Universal Descriptors: Charge Fingerprints , 2009, J. Chem. Inf. Model..

[41]  Vladimir Poroikov,et al.  Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds , 2000, J. Chem. Inf. Comput. Sci..

[42]  J. Sutherland,et al.  A comparison of methods for modeling quantitative structure-activity relationships. , 2004, Journal of medicinal chemistry.