Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting

被引：18
|
作者：
Tetko, IV
Villa, AEP
Aksenova, TI
Zielinski, WL
Brower, J
Collantes, ER
Welsh, WJ
机构：
[1] Univ Lausanne, Inst Physiol, Lab Neuroheurist, CH-1005 Lausanne, Switzerland
[2] Inst Bioorgan & Petr Chem, Dept Biomed Applicat, UA-253660 Kiev 660, Ukraine
[3] Inst Appl Syst Anal, UA-252056 Kiev, Ukraine
[4] US FDA, Div Drug Anal, St Louis, MO 63101 USA
[5] Univ Missouri, Dept Chem, St Louis, MO 63121 USA
[6] Univ Missouri, Ctr Mol Elect, St Louis, MO 63121 USA
来源：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 1998年 / 38卷 / 04期
关键词：
D O I：
10.1021/ci970439j
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceutical fingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameter data set. A localized fingerprint region was identified within the original input parameter space from which a subset of input parameters was extracted leading to enhanced ANN performance. The present results confirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting.
引用
收藏
页码：660 / 668
页数：9
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