Electron Density Fingerprints (EDprints): Virtual Screening Using Assembled Information of Electron Density

We have designed a method to encode properties related to the electron densities of molecules (calculated (1)H and (13)C NMR shifts and atomic partial charges) in molecular fingerprints (EDprints). EDprints was evaluated in terms of their retrospective virtual screening accuracy against the Directory of Useful Decoys (DUD) and compared to the established ligand-based similarity search methods MOLPRINT 2D and FCFP-4. Although there are no significant differences in the overall virtual screening accuracies of the three methods, specific examples highlight interesting differences between the new EDprints fingerprint method and the atom-centered circular fingerprint methods of MOLPRINT 2D and FCFP-4. On one hand, EDprints similarity searches can be biased by the molecular protonation state, especially when reference ligands contain multiple ionizable groups. On the other hand, EDprints models are more robust toward subtle rearrangements of chemical groups and more suitable for screening against reference molecules with fused ring systems than MOLPRINT 2D and FCFP-4. EDprints is furthermore the fastest method under investigation in comparing fingerprints (average 56-233-fold increase in speed), which makes it highly suitable for all-against-all similarity searches and for repetitive virtual screening against large chemical databases of millions of compounds.

[1]  Lirong Chen,et al.  Mapping the Binding Site of a Large Set of Quinazoline Type EGF-R Inhibitors Using Molecular Field Analyses and Molecular Docking Studies. , 2003 .

[2]  Mark S. Johnson,et al.  Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm , 2007, J. Chem. Inf. Model..

[3]  Lucas Visscher,et al.  NMR solvent shifts of acetonitrile from frozen density embedding calculations. , 2008, The journal of physical chemistry. A.

[4]  J. Irwin,et al.  ZINC ? A Free Database of Commercially Available Compounds for Virtual Screening. , 2005 .

[5]  Morton E. Munk,et al.  C13Shift: a computer program for the prediction of carbon-13 NMR spectra based on an open set of additivity rules , 1992, J. Chem. Inf. Comput. Sci..

[6]  Willem P. van Hoorn,et al.  Designing Compound Subsets: Comparison of Random and Rational Approaches Using Statistical Simulation , 2007, J. Chem. Inf. Model..

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

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

[9]  Lorenz C. Blum,et al.  970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. , 2009, Journal of the American Chemical Society.

[10]  Andreas Bender,et al.  How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space , 2009, J. Chem. Inf. Model..

[11]  Ajay N. Jain,et al.  Molecular Shape and Medicinal Chemistry: A Perspective , 2010, Journal of medicinal chemistry.

[12]  John M. Barnard,et al.  Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..

[13]  Ajay N. Jain,et al.  Recommendations for evaluation of computational methods , 2008, J. Comput. Aided Mol. Des..

[14]  Thierry Kogej,et al.  Multifingerprint Based Similarity Searches for Targeted Class Compound Selection , 2006, J. Chem. Inf. Model..

[15]  Simona Distinto,et al.  How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information , 2009, J. Chem. Inf. Model..

[16]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[17]  Andreas Bender,et al.  Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance , 2004, J. Chem. Inf. Model..

[18]  Morton E. Munk,et al.  Spectra Estimation for Computer-Aided Structure Determination , 1996, J. Chem. Inf. Comput. Sci..

[19]  Z. Deng,et al.  Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors. , 2006, Journal of medicinal chemistry.

[20]  D. J. Price,et al.  Assessing scoring functions for protein-ligand interactions. , 2004, Journal of medicinal chemistry.

[21]  Richard A. Lewis,et al.  Three-dimensional pharmacophore methods in drug discovery. , 2010, Journal of medicinal chemistry.

[22]  Weifan Zheng,et al.  Unconventional 2D Shape Similarity Method Affords Comparable Enrichment as a 3D Shape Method in Virtual Screening Experiments , 2009, J. Chem. Inf. Model..

[23]  Lisa Harris,et al.  Partial Charge Calculation Method Affects CoMFA QSAR Prediction Accuracy , 2009, J. Chem. Inf. Model..

[24]  Alexander M. Lewis,et al.  Identification of a chemical probe for NAADP by virtual screening , 2009, Nature chemical biology.

[25]  P. Willett Searching techniques for databases of two- and three-dimensional chemical structures. , 2005, Journal of medicinal chemistry.

[26]  Piotr Cieplak,et al.  The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building. , 2010, Physical chemistry chemical physics : PCCP.

[27]  Thomas Sander,et al.  Comparison of Ligand- and Structure-Based Virtual Screening on the DUD Data Set , 2009, J. Chem. Inf. Model..

[28]  Karen Spärck Jones A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[29]  Johann Gasteiger,et al.  A new model for calculating atomic charges in molecules , 1978 .

[30]  Gilles Marcou,et al.  Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints , 2007, J. Chem. Inf. Model..

[31]  D. Rognan Chemogenomic approaches to rational drug design , 2007, British journal of pharmacology.

[32]  Ulrich Rester,et al.  From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. , 2008, Current opinion in drug discovery & development.

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

[34]  Yongbo Hu,et al.  Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..

[35]  R. Glen,et al.  Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets , 2005, Journal of biomolecular screening.