Similarity metrics and descriptor spaces - Which combinations to choose?

被引:42
作者
Glen, Robert C. [1 ]
Adams, Samuel E. [1 ]
机构
[1] Univ Cambridge, Dept Chem, Unilever Ctr Mol Sci Informat, Cambridge CB2 1EW, England
来源
QSAR & COMBINATORIAL SCIENCE | 2006年 / 25卷 / 12期
关键词
descriptors; diversity; high-throughput; in-silico; machine learning; metrics; QSAR; screening; similarity; structure activity; virtual library;
D O I
10.1002/qsar.200610097
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Molecular similarity is widely used in virtual screening. There are a very large number of possible combinations of molecular descriptors and analysis methods that can be combined to pre-select compounds. The objectives strongly influence the methods chosen, in particular whether the desired outcome is to design a diverse library for initial screening; to follow up with additional similar hits (to perhaps help in establishing SAR) or to discover novel scaffolds (lead hopping) with the objective of obtaining novel patentable series (perhaps with different pharmacokinetics). Some of the factors that influence these decisions are discussed along with applications that compare and contrast methods and their performance in different situations.
引用
收藏
页码:1133 / 1142
页数:10
相关论文
共 122 条
  • [41] Gibbs AC, 2006, RSC BIOMOLEC SCI, P137
  • [42] Selecting combinatorial libraries to optimize diversity and physical properties
    Gillet, VJ
    Willett, P
    Bradshaw, J
    Green, DVS
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1999, 39 (01): : 169 - 177
  • [43] Combinatorial library design using a multiobjective genetic algorithm
    Gillet, VJ
    Khatib, W
    Willett, P
    Fleming, PJ
    Green, DVS
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2002, 42 (02): : 375 - 385
  • [44] Analysis of activity space by fragment fingerprints, 2D descriptors, and multitarget dependent transformation of 2D descriptors
    Givehchi, Alireza
    Bender, Andreas
    Glen, Robert C.
    [J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2006, 46 (03) : 1078 - 1083
  • [45] GLEN RC, 1995, J MED CHEM, V38, P3566
  • [46] Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers
    Glick, M
    Jenkins, JL
    Nettles, JH
    Hitchings, H
    Davies, JW
    [J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2006, 46 (01) : 193 - 200
  • [48] Assessing model fit by cross-validation
    Hawkins, DM
    Basak, SC
    Mills, D
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2003, 43 (02): : 579 - 586
  • [49] Combining chemodescriptors and biodescriptors in quantitative structure-activity relationship modeling
    Hawkins, DM
    Basak, SC
    Kraker, J
    Geiss, KT
    Witzmann, FA
    [J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2006, 46 (01) : 9 - 16
  • [50] The problem of overfitting
    Hawkins, DM
    [J]. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2004, 44 (01): : 1 - 12