Strategies for subset selection of parts of an in-house chemical library

被引:10
作者
Andersson, PM [1 ]
Sjöström, M
Wold, S
Lundstedt, T
机构
[1] Umea Univ, Dept Chem, Chemometr Res Grp, SE-90187 Umea, Sweden
[2] Melacure Therapeut AB, SE-75643 Uppsala, Sweden
[3] Uppsala Univ, BMC, Dept Organ Pharmaceut Chem, SE-75123 Uppsala, Sweden
关键词
selection; chemical libraries; D-optimal; cell-based design; statistical molecular design;
D O I
10.1002/cem.671.abs
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
When a company decides to perform biological testing of their 'in-house' library, i.e. compounds which have been synthesized or purchased over the years, it is usually not feasible or desirable to test all of them using e.g. high-throughput screening (HTS). The limitation is the usually high number of compounds to test (10(4)-10(6)) leading to practical limitations and high costs in terms of both material costs and disposal considerations, Therefore it is often desirable to make a selection of which compounds to include in the biological testing. A challenge is how to make this selection in order to cover the structural space of the in-house library as well as possible. Here we present and discuss different selection strategies based mainly on statistical molecular design (SMD). These methods require different prior information about the compounds under investigation, e.g. characterization of the chemical structure, affinity/biological activity data or neither of these. Which method to be used is largely problem-dependent, i.e. the composition and origin of the library, and hence the structural space, are of great importance. Chemical and biological knowledge about the system under investigation should as far as possible be considered when making the final decision on which method to apply. Copyright (C) 2001 John Wiley & Sons, Ltd.
引用
收藏
页码:353 / 369
页数:17
相关论文
共 42 条
  • [1] ABRAMO L, 1994, Patent No. 9403436
  • [2] Andersson PM, 1999, MOLECULAR DIVERSITY IN DRUG DESIGN, P197
  • [3] ANDERSSON PM, 2000, J CHEMOMETR, V14, P1
  • [4] CLUSTERING OF CHEMICAL STRUCTURES ON THE BASIS OF 2-DIMENSIONAL SIMILARITY MEASURES
    BARNARD, JM
    DOWNS, GM
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1992, 32 (06): : 644 - 649
  • [5] D-OPTIMAL DESIGNS IN QSAR
    BARONI, M
    CLEMENTI, S
    CRUCIANI, G
    KETTANEHWOLD, N
    WOLD, S
    [J]. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS, 1993, 12 (03): : 225 - 231
  • [6] On the equivalence between different descriptions of molecules: Value for computational approaches
    Benigni, R
    Gallo, G
    Giorgi, F
    Giuliani, A
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1999, 39 (03): : 575 - 578
  • [7] CLUSTER-ANALYSIS
    BRATCHELL, N
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1989, 6 (02) : 105 - 125
  • [8] Broach JR, 1996, NATURE, V384, P14
  • [9] SCREENING OF SUITABLE SOLVENTS IN ORGANIC-SYNTHESIS - STRATEGIES FOR SOLVENT SELECTION
    CARLSON, R
    LUNDSTEDT, T
    ALBANO, C
    [J]. ACTA CHEMICA SCANDINAVICA SERIES B-ORGANIC CHEMISTRY AND BIOCHEMISTRY, 1985, 39 (02): : 79 - 91
  • [10] CLARK RD, 1997, TAMING COMBINATORIAL, P24