Commercial software systems for diversity analysis

while many computational chemistry teams in the pharmaceutical industry have developed proprietary software for analyzing the diversity of libraries, many of them have done so using tools supplied by commercial vendors of computational chemistry software. It is often more efficient for a drug company to license (and probably then customize) commercially available software than to employ teams of programmers in-house to generate proprietary software from scratch. This paper discusses some of the best-known commercially available software for selecting diverse sets of starting materials, for building focused or diverse virtual libraries, for studying the structural overlap of databases and for studying the coverage of structural space.

[1]  Stephen D. Pickett,et al.  Diversity Profiling and Design Using 3D Pharmacophores: Pharmacophore-Derived Queries (PDQ) , 1996, J. Chem. Inf. Comput. Sci..

[2]  Andrew Smellie,et al.  Identification of Common Functional Configurations Among Molecules , 1996, J. Chem. Inf. Comput. Sci..

[3]  James G. Nourse,et al.  Managing the Combinatorial Explosion , 1997, J. Chem. Inf. Comput. Sci..

[4]  D. Rogers,et al.  Receptor surface models. 2. Application to quantitative structure-activity relationships studies. , 1995, Journal of medicinal chemistry.

[5]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[6]  D C Spellmeyer,et al.  Measuring diversity: experimental design of combinatorial libraries for drug discovery. , 1995, Journal of medicinal chemistry.

[7]  John M. Barnard,et al.  Chemical Fragment Generation and Clustering Software , 1997, J. Chem. Inf. Comput. Sci..

[8]  Steven L. Teig,et al.  Chemical Function Queries for 3D Database Search , 1994, J. Chem. Inf. Comput. Sci..

[9]  Andrew Smellie,et al.  Analysis of Conformational Coverage, 1. Validation and Estimation of Coverage , 1995, J. Chem. Inf. Comput. Sci..

[10]  Peter Willett,et al.  Rapid Quantification of Molecular Diversity for Selective Database Acquisition , 1997, J. Chem. Inf. Comput. Sci..

[11]  A. Ghose,et al.  Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity , 1986 .

[12]  J. Mason,et al.  New perspectives in lead generation II: Evaluating molecular diversity , 1996 .

[13]  Andrew Smellie,et al.  Analysis of Conformational Coverage, 2. Applications of Conformational Models , 1995, J. Chem. Inf. Comput. Sci..

[14]  John P. Devlin,et al.  High Throughput Screening: The Discovery of Bioactive Substances , 1997 .

[15]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[16]  P. Willett,et al.  A Fast Algorithm For Selecting Sets Of Dissimilar Molecules From Large Chemical Databases , 1995 .

[17]  Robert D Clark,et al.  Bioisosterism as a molecular diversity descriptor: steric fields of single "topomeric" conformers. , 1996, Journal of medicinal chemistry.

[18]  David Weininger,et al.  CHORTLES: A Method for Representing Oligomeric and Template-Based Mixtures , 1995, J. Chem. Inf. Comput. Sci..

[19]  Mathew Hahn,et al.  Three-Dimensional Shape-Based Searching of Conformationally Flexible Compounds , 1997, J. Chem. Inf. Comput. Sci..

[20]  Anthony W. Czarnik,et al.  A practical guide to combinatorial chemistry , 1997 .

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

[22]  David Weininger,et al.  Stigmata: An Algorithm To Determine Structural Commonalities in Diverse Datasets , 1996, J. Chem. Inf. Comput. Sci..

[23]  David Weininger,et al.  SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..

[24]  F. Burden Molecular identification number for substructure searches , 1989, J. Chem. Inf. Comput. Sci..

[25]  M. Hahn Receptor surface models. 1. Definition and construction. , 1995, Journal of medicinal chemistry.

[26]  David Weininger,et al.  CHUCKLES: A method for representing and searching peptide and peptoid sequences on both monomer and atomic levels , 1994, J. Chem. Inf. Comput. Sci..

[27]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[28]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[29]  Andrew Smellie,et al.  Poling: Promoting conformational variation , 1995, J. Comput. Chem..

[30]  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.

[31]  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..