Combichem and cheminformatics

Current Opinion in Drug Discovery and Development

Volumn 2, Issue 3, 1999, Pages 197-203

  Polinsky, Alexander ^a  

^a AGOURON PHARMACEUTICALS INC   (United States)

Author keywords

Chemoinformatics; Combinatorial libraries; Diversity; Drug like

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CHEMICAL STRUCTURE; COMPUTER PROGRAM; DATA ANALYSIS; DATA BASE; DRUG INDUSTRY; INFORMATION PROCESSING; INFORMATION SCIENCE; SYNTHESIS;

EID: 0033009015     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (49)

1. Brown FK: Chemoinformatics: What is It and how does it impact drug discovery. In: Ann Rep Med Chem. Bristol JA (Ed), Academic Press, San Diego, CA, USA (1998) 33:375-384.
2. Blaney JM, Martin EJ: Computational approaches for combinatorial library design and molecular diversity analysis. Curr Opin Chem Biol (1997) 1:54-59.
3. Bures MG, Martin YC: Computational methods In molecular diversity and combinatorial chemistry. Curr Opin Chem Biol (1998) 2:376-380.
4. Clark RD, Cramer RD: Taming the combinatorial centipede. CHEMTECH (1997) 27:24-30.
5. CombiChem. Daylight Chemical Information Systems Inc, Mission Viejo, CA, USA.
6. Legion. Tripos Inc, St Louis, MO, USA.
7. Central Library. MDL Information Systems, San Leandro, CA, USA.
8. Leland BA, Christie BD, Nourse JG, Grier DL, Carhart RE, Maffett T, Welford SM, Smith DH: Managing the combinatorial explosion. J Chem Inf Comput Sei (1997) 37:62-70.
9. Polinsky A, Feinstein RD, Shi S, Kuki A: LiBrain: Software for automated design of exploratory and targeted combinatorial libraries. In: Molecular Diversity and Combinatorial Chemistry, Chaiken IM, Janda KD (Eds). American Chemical Society, MA, USA (1996):219-232.
10. A concept of chemically accessible virtual library is introduced as the basis for the automated library design and information management technology. The software system, LiBrain, accumulates empirical information about combinatorial reactions; this chemical intelligence is then used to define chemically accessible space in which library design is performed.
11. Cramer RD, Clark RD, Patterson DE, Ferguson AM: Bioisosterism as a molecular diversity descriptor: Steric fields of single 'topomeric' conformers. J Med Chem (1996) 39:3060-3069.
12. Lewis RA, Mason JS, McLay IM: Similarity measures for rational set selection and analysis of combinatorial libraries: The diverse property-derived (DPD) approach. J Chem Inf Comput Sei (1997) 37:599-614.
13. Martin EJ, Blaney JM, Siani MA, Spellmeyer DC, Wong AK, Moos WH: Measuring diversity: Experimental design of combinatorial libraries for drug discovery. J Med Chem (1995)38:1431-1436.
14. First comprehensive description of the combinatorial library design concept Novel molecular descriptors are introduced. Experimental design methods are used to select building blocks for actual library synthesis.
15. Menard PR, Mason JS, Morize I, Bauerschmidt S: Chemistry space metrics in diversity analysis, library design, and compound selection. J Chem Inf Comput Sei (1998) 38:1204-1213.
16. Peartman RS, Smith KM: Novel software tools for chemical diversity. Persp Drug Discov Design (1998) 9:339-353.
17. Novel, low dimensionality, BCUT diversity descriptors are introduced and used in a cell-based library design procedure. A systematic approach to descriptor validation is also described.
18. Willett P: Using computational tools to analyze molecular diversity. In: A Practical Guide to Combinatorial Chemistry, Czamik AW, Dewitt SH (Eds). American Chemical Society, Washington DC, USA (1997):17-48. A comprehensive review of computational methods used in combinatorial chemistry - from selection and validation of molecular descriptors to the analysis of various diversity metrics and their use in library design.
19. Brown RD, Martin YC: Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci (1996) 36:572-584.
20. Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE: Neighborhood behavior: A useful concept for validtion of 'molecular diversity' descriptors. J Med Chem (1996) 39:3049-3059.
21. Walters WP, Stahl MT, Murcko MA: Virtual screening - an overview. Drug Discov Today (1998) 3:160-178.
22. Holliday JD, Ranade SS, Willett PA: Fast algorithm for selecting sets of dissimilar molecules from large chemical databases. Quantitative Structure-Activity Relationships (1995) 14:501-506.
23. Mason JS, Pickett SD: Partition-based selection. Persp Drug Discov Design (1997) 7/8:85-114.
24. Fecik RA, Frank KE, Gentry EJ, Menon SR, Mitscher LA, Telikepalü H: The search for orally active medications through combinatorial chemistry. Med Res Rev (1998) 18:149-185.
25. Upinski CA, Lcmbardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev (1997) 23:3-25.
26. Martin EJ, Critchlow RE: Beyond mere diversity: Tailoring combinatorial libraries for drug discovery. J Comb Chem (1999)1:32-45.
27. An attempt is made to incorporate explicitly accumulated medicinal chemistry experience into library design. Molecular features related to the drug-likeness of a molecule and its synthetic accessibility are captured by special descriptors that are used on top of more conventional diversity descriptors to select builolng blocks.
28. Walters WP, Murcko MA, Ajay: Can we learn to distinguish between 'drug-like' and 'nondrug-like molecules? J Med Chem (1998) 41:3314-324.
29. An effective neural network-based classifier for distinguishing between drugs and non-drugs is built and trained. A computer experiment demonstrates a significant increase in the likelihood of finding drug-like molecules compared to a random selection.
30. Sadowski J, Kubinyi H: A scoring scheme for discriminating between drugs and nondrugs. J Med Chem (1998) 41:3325-3329.
31. Describes a neural network based classifier for discriminating between drugs and non-drugs. This classifier can be instrumental for enriching combinatorial libraries by drug-like structures.
32. Ghose AK, Viswanadharrj VN, Wendoloski JJ: A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of know drug databases. J Comb Chem (1999) 1:55-68.
33. Available Chemicals Directory Database. Distributed by MDL Information Systems Inc. San Leandro, CA, USA.
34. Derwent World Drug Index. Derwent Information Ltd, London, UK.
35. Comprehensive Medicinal Chemistry Database. Distributed by MDL Information Systems Inc, San Leandro, CA, USA.
36. Ghose AK, Crippen GM: Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem (1986) 7:565-577.
37. SSKEYS. MDL Information Systems Inc, San Leandro, CA, USA.
38. MACCS-II Drug Data Report Database. Distributed by MDL Information Systems Ino, San Leandro, CA, USA.
39. Pickett SD, Luttmann C, Guerin V, Laoui A, James E: DIVSEL and COMPLIB - Strategies for the design and comparison of combinatorial libraries using pharmacophoric descriptors. J Chem InfComp Sci (1998) 38:144-150.
40. Cramer RD, Patterson DE, Bunce JD: Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc (1988)110:5959-5967.
41. Kubinyi H: Combinatorial and computational approaches in structure-based drug design. Curr Opin Drug Discov Dev (1998) 1:16-27.
42. Nickeil DG: Finding a needle in a haystack: Information management for high-throughput synthesis of small organic molecules. Am Lab (1998) 30:34-36.
43. Leweil XQ, Judd DB, Watson SP, Harm MM: RECAP-Retrosynthetic combinatorial analysis procedure: A powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comp Sci (1998) 38:511 -522.
44. A procedure is designed to identify useful building blocks by fragmenting a set of biologically active molecules around bonds that are formed by common chemical reactions. The libraries designed using identified fragments are chemically accessible by definition. Libraries can be focused on specific biological targets by selecting molecules with a certain type of activity for fragmentation.
45. Kubinyi H: QSAR: Hansch analysis and related approaches. In: Methods and principles in medicinal chemistry. Volume 1. Weinheim, New York (1993).
46. Lapute P: Binary QSAR: A new method for the determination of quantitative structure activity relationships. In: Pacific Symposium on Biocomputing '99, Altman RB, ef al (Eds). World Scientific Publishing Co, River Edge, NJ (1999):444-455.
47. QuaSAR-Binary. Chemical Computing Group Inc, Montreal, Canada.
48. Tarbit MH, Berman J: High-throughput approaches for evaluating absorption, distribution, metabolism and excretion properties of lead compounds. Curr Opin Chem Biol (1998) 2:411-416.
49. Taylor EW, Qian MG, Dollinger GD: Simultaneous on-line characterization of small organic molecules derives from combinatorial libraries for identity, quantity, and purity by reversed-phase HPLC with chemiluminescent nitrogen, UV and mass spectrometric detection. Anal Chem (1998) 70:3339-3347.