Identification of biological activity profiles using substructural analysis and genetic algorithms
被引:141
作者:
Gillet, VJ
论文数: 0引用数: 0
h-index: 0
机构:Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England
Gillet, VJ
Willett, P
论文数: 0引用数: 0
h-index: 0
机构:Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England
Willett, P
Bradshaw, J
论文数: 0引用数: 0
h-index: 0
机构:Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England
Bradshaw, J
机构:
[1] Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England
[2] Univ Sheffield, Krebs Inst Biomolec Res, Sheffield S10 2TN, S Yorkshire, England
[3] Glaxo Wellcome Res & Dev Ltd, Stevenage SG1 2NY, Herts, England
来源:
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
|
1998年
/
38卷
/
02期
关键词:
D O I:
10.1021/ci970431+
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
A substructural analysis approach is used to calculate biological activity profiles, which contain weights that describe the differential occurrences of generic features (specifically, the numbers of hydrogen-bond donors and accepters, the numbers of rotatable bonds and aromatic rings, the molecular weights, and the (2) kappa(alpha) shape descriptors) in active molecules taken from the World Drug Index and in (presumed) inactive molecules taken from the SPRESI database. Even with such simple structural descriptors, the profiles discriminate effectively between active and inactive compounds. The effectiveness of the approach is further increased by using a genetic algorithm for the calculation of the weights comprising a profile. The methods have been successfully applied to a number of different data sets.