Neighborhood Behavior: Validation of Two‐Dimensional Molecular Similarity as a Predictor of Similar Biological Activities and Docking Scores

We have used four large datasets to determine the extent to which Two-Dimensional (2D) structural similarity of chemical compounds predicts how similarly they bind to proteins. Structures of 1750, 1853, 1377, and 407 inhibitors for trypsin, thrombin, factor Xa (fXa), and urokinase-type Plasminogen Activator (uPA), respectively, with their observed binding affinities were collected from various literature sources. We also obtained calculated binding affinities for the datasets using an in-house docking program. Plots of the differences in affinity for all pairs of compounds versus their 2D similarity values were generated. All datasets with both observed and calculated affinities clearly exhibit structure–activity relationships (neighborhood behavior), though notable differences among plots are also observed. Guidelines for application of 2D similarity in structure-based virtual screening are discussed in the context of the results obtained.

[1]  B. Fan,et al.  Molecular similarity and diversity in chemoinformatics: From theory to applications , 2006, Molecular Diversity.

[2]  C. Supuran,et al.  Therapeutic applications of serine protease inhibitors , 2002 .

[3]  Robert D. Clark,et al.  Virtual Compound Libraries: A New Approach to Decision Making in Molecular Discovery Research , 1998, J. Chem. Inf. Comput. Sci..

[4]  D. Horvath,et al.  Neighborhood behavior. Fuzzy molecular descriptors and their influence on the relationship between structural similarity and property similarity , 2003 .

[5]  Jürgen Bajorath,et al.  Anatomy of Fingerprint Search Calculations on Structurally Diverse Sets of Active Compounds , 2005, J. Chem. Inf. Model..

[6]  Robert D Clark,et al.  Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. , 1996, Journal of medicinal chemistry.

[7]  Dragos Horvath,et al.  Neighborhood Behavior of in Silico Structural Spaces with Respect to In Vitro Activity Spaces-A Benchmark for Neighborhood Behavior Assessment of Different in Silico Similarity Metrics , 2003, J. Chem. Inf. Comput. Sci..

[8]  P. Willett,et al.  A Comparison of Some Measures for the Determination of Inter‐Molecular Structural Similarity Measures of Inter‐Molecular Structural Similarity , 1986 .

[9]  Dragos Horvath,et al.  Molecular similarity and property similarity. , 2004, Current topics in medicinal chemistry.

[10]  Jennifer R. Krumrine,et al.  Statistical tools for virtual screening. , 2005, Journal of medicinal chemistry.

[11]  Y. Cheng,et al.  Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. , 1973, Biochemical pharmacology.

[12]  Elizabeth Wilson,et al.  Is safe exchange of data possible , 2005 .

[13]  D. N. Tarasov,et al.  A novel scoring function for molecular docking , 2003, J. Comput. Aided Mol. Des..

[14]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[15]  Denis Khachko,et al.  A very large diversity space of synthetically accessible compounds for use with drug design programs , 2005, J. Comput. Aided Mol. Des..

[16]  Andrew Smellie,et al.  Surrogate docking: structure-based virtual screening at high throughput speed , 2005, J. Comput. Aided Mol. Des..

[17]  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..

[18]  H. Hartman,et al.  3-[[(Aryloxy)alkyl]piperidinyl]-1,2-benzisoxazoles as D2/5-HT2 antagonists with potential atypical antipsychotic activity: antipsychotic profile of iloperidone (HP 873). , 1995, Journal of medicinal chemistry.

[19]  C Silipo,et al.  Correlation analysis. Its application to the structure-activity relationship of triazines inhibiting dihydrofolate reductase. , 1975, Journal of the American Chemical Society.

[20]  Andrew C. Good,et al.  Measuring CAMD technique performance: A virtual screening case study in the design of validation experiments , 2004, J. Comput. Aided Mol. Des..

[21]  Yvonne C. Martin,et al.  The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding , 1997, J. Chem. Inf. Comput. Sci..

[22]  Hans-Jörg Roth,et al.  There is no such thing as 'diversity'! , 2005, Current opinion in chemical biology.

[23]  H. Matter,et al.  Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional molecular descriptors. , 1997, Journal of medicinal chemistry.

[24]  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..

[25]  S. L. Dixon,et al.  One-dimensional molecular representations and similarity calculations: methodology and validation. , 2001, Journal of medicinal chemistry.

[26]  N. Nikolova,et al.  International Union of Pure and Applied Chemistry, LUMO energy ± The Lowest Unoccupied Molecular Orbital (LUMO) , 2022 .

[27]  G. Klebe,et al.  Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. , 2002, Angewandte Chemie.

[28]  G. D. Perekhodtsev.  Similarity study of serine proteases inhibitors , 2006, Molecular Diversity.

[29]  R. Glen,et al.  Molecular similarity: a key technique in molecular informatics. , 2004, Organic & biomolecular chemistry.