SHED: Shannon Entropy Descriptors from Topological Feature Distributions

A novel set of molecular descriptors called SHED (SHannon Entropy Descriptors) is presented. They are derived from distributions of atom-centered feature pairs extracted directly from the topology of molecules. The value of a SHED is then obtained by applying the information-theoretical concept of Shannon entropy to quantify the variability in a feature-pair distribution. The collection of SHED values reflecting the overall distribution of pharmacophoric features in a molecule constitutes its SHED profile. Similarity between pairs of molecules is then assessed by calculating the Euclidean distance of their SHED profiles. Under the assumption that molecules having similar pharmacological profiles should contain similar features distributed in a similar manner, examples are given to show the ability of SHED for scaffold hopping in virtual chemical screening and pharmacological profiling compared to that of substructural BCI fingerprints and three-dimensional GRIND descriptors.

[1]  Steven L. Teig,et al.  Luddite: An Information-Theoretic Library Design Tool , 2003, J. Chem. Inf. Comput. Sci..

[2]  Claude E. Shannon,et al.  The Mathematical Theory of Communication , 1950 .

[3]  D Horvath,et al.  From hit to lead. Analyzing structure-profile relationships. , 2001, Journal of medicinal chemistry.

[4]  Dragos Horvath,et al.  Neighborhood Behavior of in Silico Structural Spaces with Respect to in Vitro Activity Spaces-A Novel Understanding of the Molecular Similarity Principle in the Context of Multiple Receptor Binding Profiles , 2003, J. Chem. Inf. Comput. Sci..

[5]  Marvin Johnson,et al.  Concepts and applications of molecular similarity , 1990 .

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

[7]  Jérôme Hert,et al.  Comparison of Fingerprint-Based Methods for Virtual Screening Using Multiple Bioactive Reference Structures , 2004, J. Chem. Inf. Model..

[8]  Jordi Mestres,et al.  A General Analysis of Field-Based Molecular Similarity Indices , 2002 .

[9]  R. Villalobos-Molina,et al.  Evidence that the hypotensive effect of WAY 100635, a 5-HT1A receptor antagonist, is related to vascular alpha 1-adrenoceptor blockade in the adult rat. , 2002, Autonomic & autacoid pharmacology.

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

[11]  Michael I. Jordan,et al.  Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Jürgen Bajorath,et al.  Variability of Molecular Descriptors in Compound Databases Revealed by Shannon Entropy Calculations , 2000, J. Chem. Inf. Comput. Sci..

[13]  Yvonne C. Martin,et al.  Use of Structure-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection , 1996, J. Chem. Inf. Comput. Sci..

[14]  G. Makara,et al.  Measuring molecular similarity and diversity: total pharmacophore diversity. , 2001, Journal of medicinal chemistry.

[15]  Jordi Mestres,et al.  Putting molecular similarity into context: asymmetric indices for field-based similarity measures , 2006 .

[16]  A. Buras,et al.  A general analysis of , 1998, hep-ph/9810260.

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

[18]  Jürgen Bajorath,et al.  Distinguishing between Natural Products and Synthetic Molecules by Descriptor Shannon Entropy Analysis and Binary QSAR Calculations , 2000, J. Chem. Inf. Comput. Sci..

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

[20]  Lin Xia,et al.  Pharmacophore identification of α1A-adrenoceptor antagonists , 2005 .

[21]  S. Pickett,et al.  GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. , 2000, Journal of medicinal chemistry.

[22]  Daniel J. Graham,et al.  Information Content in Organic Molecules: Aggregation States and Solvent Effects , 2005, J. Chem. Inf. Model..

[23]  Pierre Acklin,et al.  Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins , 2003, J. Chem. Inf. Comput. Sci..

[24]  Robert P. Sheridan,et al.  Chemical Similarity Using Geometric Atom Pair Descriptors , 1996, J. Chem. Inf. Comput. Sci..

[25]  A. Leo,et al.  Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology. , 2002, Chemical reviews.

[26]  M. Millan,et al.  Differential Actions of Antiparkinson Agents at Multiple Classes of Monoaminergic Receptor. II. Agonist and Antagonist Properties at Subtypes of Dopamine D2-Like Receptor and α1/α2-Adrenoceptor , 2002, Journal of Pharmacology and Experimental Therapeutics.

[27]  Lin Xia,et al.  Pharmacophore identification of alpha(1A)-adrenoceptor antagonists. , 2005, Bioorganic & medicinal chemistry letters.

[28]  Fumiko Suzuki,et al.  Binding and Functional Affinity of Sarpogrelate, Its Metabolite M-1 and Ketanserin for Human Recombinant Alpha-1-Adrenoceptor Subtypes , 2002, Pharmacology.