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Neural networks as data mining tools in drug design
Author(s) -
Gasteiger Johann,
Teckentrup Andreas,
Terfloth Lothar,
Spycher Simon
Publication year - 2003
Publication title -
journal of physical organic chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 66
eISSN - 1099-1395
pISSN - 0894-3230
DOI - 10.1002/poc.597
Subject(s) - artificial neural network , chemistry , artificial intelligence , key (lock) , representation (politics) , machine learning , computer science , quantitative structure–activity relationship , data mining , computer security , politics , political science , law
Neural networks are powerful data mining tools with a wide range of applications in drug design. This paper largely concentrates on self‐organizing neural networks that can be used for investigating datasets both by unsupervised and by supervised learning. The representation of chemical structures is the key to success in establishing useful relationships. Applications are shown for exploring different structure representations, for establishing quantitative structure–activity relationships and for handling compounds having multicategory activities. The applications comprise the separation of compounds according to different biological activities, the location of biologically active compounds in large chemical spaces, the analysis of high‐throughput screening data and the classification of compounds according to mode of toxic action. Copyright © 2003 John Wiley & Sons, Ltd.