Matrix‐based Molecular Descriptors for Prospective Virtual Compound Screening

Molecular descriptors capture diverse structural information of molecules and are a prerequisite for ligand‐based similarity searching. In this study, we introduce topological matrix‐based descriptors to virtual screening for hit discovery. We evaluated the usefulness of matrix‐based descriptors in a retrospective setting and compared them with topological pharmacophore descriptors. Special attention was given to the influence of data pre‐processing and the applied similarity metric on the virtual screening performance. Overall, the MB descriptors showed a competitive and complementary performance to other descriptors. A prospective screen of a commercial compound library led to the discovery of a novel natural‐product‐derived cyclooxygenase‐2 inhibitor predicted to interact differently with the target protein compared to the query compound ibuprofen. The results of our study motivate the use of matrix‐based descriptors for molecular similarity‐based virtual screening and scaffold hopping.

[1]  Oliver Werz,et al.  Hyperforin is a dual inhibitor of cyclooxygenase-1 and 5-lipoxygenase. , 2002, Biochemical pharmacology.

[2]  Mahmude Özkale,et al.  Wiley StatsRef: Statistics Reference Online , 2016 .

[3]  R. Todeschini,et al.  Multivariate Analysis of Molecular Descriptors , 2012 .

[4]  G. Schneider,et al.  Revealing the Macromolecular Targets of Fragment-Like Natural Products. , 2015, Angewandte Chemie.

[5]  Petra Schneider,et al.  Revealing the macromolecular targets of complex natural products. , 2014, Nature chemistry.

[6]  Petra Schneider,et al.  Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for ‘Orphan’ Molecules , 2013, Molecular informatics.

[7]  R. Todeschini,et al.  Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling. , 2012, Chemosphere.

[8]  Petra Schneider,et al.  Multi-objective molecular de novo design by adaptive fragment prioritization. , 2014, Angewandte Chemie.

[9]  Roberto Todeschini,et al.  A new concept of higher-order similarity and the role of distance/similarity measures in local classification methods , 2016 .

[10]  O. Werz,et al.  SAR studies on curcumin's pro-inflammatory targets: discovery of prenylated pyrazolocurcuminoids as potent and selective novel inhibitors of 5-lipoxygenase. , 2014, Journal of medicinal chemistry.

[11]  Gisbert Schneider,et al.  Virtual screening: an endless staircase? , 2010, Nature Reviews Drug Discovery.

[12]  Gisbert Schneider,et al.  Collection of bioactive reference compounds for focused library design , 2003 .

[13]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[14]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[15]  Petra Schneider,et al.  Chemography of Natural Product Space , 2015, Planta Medica.

[16]  Roberto Todeschini,et al.  Quantitative Structure − Activity Relationship Models for Ready Biodegradability of Chemicals , 2013 .

[17]  Petra Schneider,et al.  Counting on natural products for drug design. , 2016, Nature chemistry.

[18]  Petra Schneider,et al.  Comparison of correlation vector methods for ligand-based similarity searching , 2003, J. Comput. Aided Mol. Des..

[19]  E. Topol,et al.  Risk of cardiovascular events associated with selective COX-2 inhibitors. , 2001, JAMA.

[20]  P Schneider,et al.  Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors† †Electronic supplementary information (ESI) available: Details about computational comparisons and all screening results. See DOI: 10.1039/c5sc04272k , 2016, Chemical science.

[21]  G. Schneider,et al.  Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands. , 2014, Angewandte Chemie.

[22]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[23]  P Schneider,et al.  Spotting and designing promiscuous ligands for drug discovery. , 2016, Chemical communications.

[24]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

[25]  Petra Schneider,et al.  De Novo Fragment Design for Drug Discovery and Chemical Biology. , 2015, Angewandte Chemie.

[26]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[27]  L. Goodman,et al.  The Pharmacological Basis of Therapeutics , 1941 .

[28]  Roberto Todeschini,et al.  In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9 , 2016, International journal of molecular sciences.

[29]  M. Malkowski,et al.  The structure of ibuprofen bound to cyclooxygenase-2. , 2014, Journal of structural biology.

[30]  R Day,et al.  Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. VIGOR Study Group. , 2000, The New England journal of medicine.

[31]  Petra Schneider,et al.  Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus , 2014, Proceedings of the National Academy of Sciences.

[32]  Ying Yu,et al.  Vascular COX-2 Modulates Blood Pressure and Thrombosis in Mice , 2012, Science Translational Medicine.

[33]  Peter Willett,et al.  The Calculation of Molecular Structural Similarity: Principles and Practice , 2014, Molecular informatics.

[34]  Roberto Todeschini,et al.  Molecular descriptors for chemoinformatics , 2009 .

[35]  R. Venkataraghavan,et al.  Atom pairs as molecular features in structure-activity studies: definition and applications , 1985, J. Chem. Inf. Comput. Sci..

[36]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[37]  C. Thiemermann,et al.  Selectivity of nonsteroidal antiinflammatory drugs as inhibitors of constitutive and inducible cyclooxygenase. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Gisbert Schneider,et al.  Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.

[39]  H. Kitasato,et al.  Cyclooxygenase‐1 and cyclooxygenase‐2 selectivity of non‐steroidal anti‐inflammatory drugs: investigation using human peripheral monocytes , 2001, The Journal of pharmacy and pharmacology.

[40]  T. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[41]  Roberto Todeschini,et al.  Qualitative consensus of QSAR ready biodegradability predictions , 2016 .

[42]  R. Todeschini,et al.  Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .