Chemical diversity as a driving force to design and put in practice synthetic strategies leading to combinatorial libraries for lead discovery and lead optimization

Publisher Summary This chapter discusses the chemical diversity as a driving force to design and put in practice synthetic strategies leading to combinatorial libraries for lead discovery and lead optimization. Target identification and target validation are now crucial milestones, as the unraveling of the human genome is providing thousands of new, uncharacterized genes as potential targets for the cure of important diseases. Research laboratories able to identify and validate targets better and faster than competitors will be significantly advantaged, and combinatorial approaches and tools will provide relevant benefits at this stage. The full potential of chemical diversity and combinatorial libraries is evident in the following three steps of the process, that is, (1) from target to hit; (2) from hit to lead; and (3) from lead to clinical candidate. Once a target is validated, the hit identification phase kicks off and chemical disciplines start to be heavily involved. While biologists develop a suitable biological assay to identify compounds interacting with the target, chemists will assemble a suitable chemical collection (diversity), which is screened on the biological assay (screening) eventually leading to active compounds, or hits (structure determination). If the target is novel and poorly characterized, chemical diversity is the main driver to assemble a screening collection. A collection spanning the so-called diversity space is more likely to identify an active compound, or hit, than a collection that is clustered in an area of the same space.

[1]  K. To Identification of differential gene expression by high throughput analysis. , 2000, Combinatorial chemistry & high throughput screening.

[2]  J H Zhang,et al.  Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations. , 2000, Journal of combinatorial chemistry.

[3]  P Willett,et al.  Chemoinformatics - similarity and diversity in chemical libraries. , 2000, Current opinion in biotechnology.

[4]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[5]  M. Sakamoto,et al.  Studies on tandem transesterification and intramolecular cycloaddition of nitrones. 2. Sequential bicyclization of α,α-dialkoxycarbonylnitrones with allyl alcohols , 1995 .

[6]  L Xue,et al.  Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. , 2000, Combinatorial chemistry & high throughput screening.

[7]  Peter Willett,et al.  Bit-String Methods for Selective Compound Acquisition , 2000, J. Chem. Inf. Comput. Sci..

[8]  C. Moallemi,et al.  Quantized surface complementarity diversity (QSCD): a model based on small molecule-target complementarity. , 2000, Journal of medicinal chemistry.

[9]  W. Ryan,et al.  Automated parallel synthesis of chalcone-based screening libraries , 1998 .

[10]  C Barnes,et al.  Recent developments in the encoding and deconvolution of combinatorial libraries. , 2000, Current opinion in chemical biology.

[11]  A Furka Redistribution in combinatorial synthesis. A theoretical approach. , 2000, Combinatorial chemistry & high throughput screening.

[12]  Stuart L. Schreiber,et al.  Synthesis and Preliminary Evaluation of a Library of Polycyclic Small Molecules for Use in Chemical Genetic Assays , 1999 .

[13]  L. Weber High-diversity combinatorial libraries. , 2000, Current opinion in chemical biology.

[14]  David E. Clark,et al.  Enhancing the Hit-to-Lead Properties of Lead Optimization Libraries , 2000, J. Chem. Inf. Comput. Sci..

[15]  G. Fassina,et al.  Combinatorial Chemistry and Technology: Principles, Methods and Applications , 1999 .

[16]  Forecasting roles of combinatorial chemistry in the age of genomically derived drug discovery targets. , 2000 .

[17]  Markus Wagener,et al.  Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features , 2000, J. Chem. Inf. Comput. Sci..

[18]  Kit S. Lam,et al.  The “One-Bead-One-Compound” Combinatorial Library Method , 1997 .

[19]  Coates,et al.  Successful implementation of automation in medicinal chemistry. , 2000, Drug discovery today.

[20]  Jürgen Bajorath,et al.  Evaluation of Descriptors and Mini-Fingerprints for the Identification of Molecules with Similar Activity , 2000, J. Chem. Inf. Comput. Sci..

[21]  Leach,et al.  The in silico world of virtual libraries. , 2000, Drug discovery today.

[22]  W. C. Still,et al.  SEQUENCE-SELECTIVE PEPTIDE BINDING WITH A PEPTIDO-A,B-TRANS-STEROIDAL RECEPTOR SELECTED FROM AN ENCODED COMBINATORIAL RECEPTOR LIBRARY , 1996 .

[23]  Peter Willett,et al.  Computational methods for the analysis of molecular diversity , 1996 .

[24]  R A Houghten,et al.  "Libraries from libraries": chemical transformation of combinatorial libraries to extend the range and repertoire of chemical diversity. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[25]  A. Harvey,et al.  Strategies for discovering drugs from previously unexplored natural products. , 2000, Drug discovery today.