Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species.

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.

[1]  Julio Caballero,et al.  Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors. , 2006, Bioorganic & medicinal chemistry.

[2]  George W. A. Milne,et al.  An Integrated in Silico Analysis of Drug-Binding to Human Serum Albumin , 2006, J. Chem. Inf. Model..

[3]  Maykel Cruz-Monteagudo,et al.  Multi-target QSPR assemble of a Complex Network for the distribution of chemicals to biphasic systems and biological tissues , 2008 .

[4]  Károly Héberger,et al.  Prediction of Anti-HIV-1 Activity of a Series of Tetrapyrrole Molecules , 2003, J. Chem. Inf. Comput. Sci..

[5]  K. Chou,et al.  Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. , 2009, Bioorganic & medicinal chemistry.

[6]  F. Prado-Prado,et al.  Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. , 2008, Current topics in medicinal chemistry.

[7]  Peter C. Jurs,et al.  QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks , 2002, J. Chem. Inf. Comput. Sci..

[8]  Ernesto Estrada,et al.  In Silico Studies toward the Discovery of New Anti-HIV Nucleoside Compounds with the Use of TOPS-MODE and 2D/3D Connectivity Indices, 1. Pyrimidyl Derivatives , 2002, J. Chem. Inf. Comput. Sci..

[9]  Miguel A. Cabrera,et al.  Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species: distribution of the antimicrobial G1 on rat tissues. , 2005, Bioorganic & medicinal chemistry letters.

[10]  Humberto González Díaz,et al.  Unified QSAR and network‐based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals , 2008, J. Comput. Chem..

[11]  Enrique Molina,et al.  3D connectivity indices in QSPR/QSAR studies. , 2001 .

[12]  K. Chou,et al.  Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. , 2008, Bioorganic & medicinal chemistry.

[13]  Lourdes Santana,et al.  Proteomics, networks and connectivity indices , 2008, Proteomics.

[14]  K. Chou,et al.  Using LogitBoost classifier to predict protein structural classes. , 2006, Journal of theoretical biology.

[15]  Lourdes Santana,et al.  A model for the recognition of protein kinases based on the entropy of 3D van der Waals interactions. , 2007, Journal of proteome research.

[16]  Kuo-Chen Chou,et al.  Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. , 2007, Protein engineering, design & selection : PEDS.

[17]  Humberto González Díaz,et al.  QSAR model for alignment‐free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP‐lattice networks , 2008, J. Comput. Chem..

[18]  Francisco Torrens,et al.  Predicting antitrichomonal activity: a computational screening using atom-based bilinear indices and experimental proofs. , 2006, Bioorganic & medicinal chemistry.

[19]  K. Chou,et al.  Prediction of linear B-cell epitopes using amino acid pair antigenicity scale , 2007, Amino Acids.

[20]  S. Vilar,et al.  Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action. , 2006, Journal of medicinal chemistry.

[21]  Kuo-Chen Chou,et al.  Boosting classifier for predicting protein domain structural class. , 2005, Biochemical and biophysical research communications.

[22]  Lourdes Santana,et al.  Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. , 2006, Bioorganic & medicinal chemistry.

[23]  Hugo Kubinyi,et al.  Quantitative structure-activity relationships (QSAR) and molecular modelling in cancer research , 2005, Journal of Cancer Research and Clinical Oncology.

[24]  Kuo-Chen Chou,et al.  Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. , 2008, Journal of theoretical biology.

[25]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[26]  Reinaldo Molina Ruiz,et al.  Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries. , 2008, Journal of combinatorial chemistry.

[27]  Maykel Cruz-Monteagudo,et al.  Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model. , 2005, Bioorganic & medicinal chemistry.

[28]  Francisco Torrens,et al.  Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification. , 2007, Journal of molecular graphics & modelling.

[29]  M. Natália D. S. Cordeiro,et al.  Desirability‐based multiobjective optimization for global QSAR studies: Application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles , 2008, J. Comput. Chem..

[30]  H.-B. Shen,et al.  Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction , 2007, Amino Acids.

[31]  Francisco Torrens,et al.  A novel approach to predict aquatic toxicity from molecular structure. , 2008, Chemosphere.

[32]  Maykel Cruz-Monteagudo,et al.  Unified drug-target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects. , 2005, European journal of medicinal chemistry.

[33]  Santiago Vilar,et al.  Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. , 2008, Current topics in medicinal chemistry.

[34]  Humberto González-Díaz,et al.  3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif. , 2006, Journal of inorganic biochemistry.

[35]  Francisco Torrens,et al.  3D-chiral quadratic indices of the 'molecular pseudograph's atom adjacency matrix' and their application to central chirality codification: classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. , 2004, Bioorganic & medicinal chemistry.

[36]  Maykel Cruz-Monteagudo,et al.  Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems , 2007, J. Comput. Chem..

[37]  R. Coppel,et al.  Malaria and the red blood cell membrane. , 2004, Seminars in hematology.

[38]  Ernesto Estrada,et al.  Automatic extraction of structural alerts for predicting chromosome aberrations of organic compounds. , 2006, Journal of molecular graphics & modelling.

[39]  Francisco Torrens,et al.  Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. , 2005, Bioorganic & medicinal chemistry.

[40]  K. Chou.  Structural bioinformatics and its impact to biomedical science. , 2004, Current medicinal chemistry.

[41]  Lourdes Santana,et al.  Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance. , 2007, Bioorganic & medicinal chemistry.

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

[43]  Kuo-Chen Chou,et al.  Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. , 2005, Biochemical and biophysical research communications.

[44]  K. Chou,et al.  Progress in computational approach to drug development against SARS. , 2006, Current medicinal chemistry.

[45]  Kuo-Chen Chou,et al.  Methodology development for predicting subcellular localization and other attributes of proteins , 2007, Expert review of proteomics.