QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ~200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ~76%, representing a 1.5-2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available.

[1]  Robert P Sheridan,et al.  Why do we need so many chemical similarity search methods? , 2002, Drug discovery today.

[2]  Dennis M. Krüger,et al.  Comparison of Structure‐ and Ligand‐Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors , 2010, ChemMedChem.

[3]  Youyong Li,et al.  ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. , 2012, Molecular pharmaceutics.

[4]  Paul D Leeson,et al.  Time-related differences in the physical property profiles of oral drugs. , 2004, Journal of medicinal chemistry.

[5]  Peter Willett,et al.  Similarity-based virtual screening using 2D fingerprints. , 2006, Drug discovery today.

[6]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[7]  Woody Sherman,et al.  Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods , 2010, J. Cheminformatics.

[8]  Maykel Pérez González,et al.  Designing Antibacterial Compounds Through a Topological Substructural Approach. , 2004 .

[9]  Sun Choi,et al.  In silico classification of adenosine receptor antagonists using Laplacian-modified naïve Bayesian, support vector machine, and recursive partitioning. , 2010, Journal of molecular graphics & modelling.

[10]  Rob D. Brown,et al.  Quantifying model errors using similarity to training data , 2010, J. Cheminformatics.

[11]  Kathrin Heikamp,et al.  How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection , 2011, J. Chem. Inf. Model..

[12]  David J Payne,et al.  Challenges of antibacterial discovery revisited , 2010, Annals of the New York Academy of Sciences.

[13]  Jürgen Bajorath,et al.  Exploring Peptide‐Likeness of Active Molecules Using 2D Fingerprint Methods. , 2007 .

[14]  Woody Sherman,et al.  Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments , 2010, J. Chem. Inf. Model..

[15]  G. Eliopoulos,et al.  The Sanford guide to antimicrobial therapy , 2010 .

[16]  D. Livermore.  Discovery research: the scientific challenge of finding new antibiotics. , 2011, The Journal of antimicrobial chemotherapy.

[17]  Xiaoyang Xia,et al.  Classification of kinase inhibitors using a Bayesian model. , 2004, Journal of medicinal chemistry.

[18]  F. Tomás-Vert,et al.  Artificial neural network applied to the discrimination of antibacterial activity by topological methods , 2000 .

[19]  Tudor I. Oprea,et al.  Is There a Difference Between Leads and Drugs? A Historical Perspective. , 2001 .

[20]  M. Vračko,et al.  Quantitative structure–activity relationship study of antitubercular fluoroquinolones , 2011, Molecular Diversity.

[21]  D. Newman,et al.  Natural products as sources of new drugs over the last 25 years. , 2007, Journal of natural products.

[22]  Facundo Pérez-Giménez,et al.  Discrimination and selection of new potential antibacterial compounds using simple topological descriptors. , 2003, Journal of molecular graphics & modelling.

[23]  Piotr Setny,et al.  Search for Novel Aminoglycosides by Combining Fragment-Based Virtual Screening and 3D-QSAR Scoring , 2009, J. Chem. Inf. Model..

[24]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[25]  María José Castro Bleda,et al.  Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds , 2004, J. Chem. Inf. Model..

[26]  Guixia Liu,et al.  Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening , 2012, J. Chem. Inf. Model..

[27]  M. Anchel,et al.  Chemical studies with pleuromutilin. , 1952, The Journal of biological chemistry.

[28]  Francisco Torrens,et al.  Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. , 2005, Bioorganic & medicinal chemistry.

[29]  Li Di,et al.  Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability , 2010, J. Comput. Aided Mol. Des..

[30]  Philip Prathipati,et al.  Global Bayesian Models for the Prioritization of Antitubercular Agents , 2008, J. Chem. Inf. Model..

[31]  Mark T. D. Cronin,et al.  Modeling Discrimination between Antibacterial and Non‐Antibacterial Activity based on 3D Molecular Descriptors , 2003 .

[32]  D. Pompliano,et al.  Drugs for bad bugs: confronting the challenges of antibacterial discovery , 2007, Nature Reviews Drug Discovery.

[33]  D. Rogers,et al.  Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up , 2005, Journal of biomolecular screening.

[34]  Henrik Kempe,et al.  QSRR analysis of β-lactam antibiotics on a penicillin G targeted MIP stationary phase , 2010, Analytical and bioanalytical chemistry.

[35]  W. Herlihy,et al.  A21978C, a complex of new acidic peptide antibiotics: isolation, chemistry, and mass spectral structure elucidation. , 1987, The Journal of antibiotics.

[36]  Sarah R. Langdon,et al.  Predicting cytotoxicity from heterogeneous data sources with Bayesian learning , 2010, J. Cheminformatics.

[37]  Mark T. D. Cronin,et al.  Structure-Based Classification of Antibacterial Activity , 2002, J. Chem. Inf. Comput. Sci..

[38]  Heinz E. Moser,et al.  Physicochemical properties of antibacterial compounds: implications for drug discovery. , 2008, Journal of Medicinal Chemistry.

[39]  Gerrit Schüürmann,et al.  Structure-based classification of antibacterial activity. , 2002, Journal of chemical information and computer sciences.

[40]  Nanda Ghoshal,et al.  Combinatorial Library Enumeration and Lead Hopping using Comparative Interaction Fingerprint Analysis and Classical 2D QSAR Methods for Seeking Novel GABAA α3 Modulators , 2009, J. Chem. Inf. Model..

[41]  William L. Jorgensen,et al.  Journal of Chemical Information and Modeling , 2005, J. Chem. Inf. Model..