In this section
@article{King:1993:10.1007/BF02174529,
author = {King, RD and Hirst, JD and Sternberg, MJE},
doi = {10.1007/BF02174529},
journal = {Perspectives in Drug Discovery and Design},
pages = {279--290},
title = {New approaches to QSAR: Neural networks and machine learning},
url = {http://dx.doi.org/10.1007/BF02174529},
volume = {1},
year = {1993}
}
TY - JOUR
AB - Neural networks and machine learning are two methods that are increasingly being used to model QSARs. They make few statistical assumptions and are nonlinear and nonparametric. We describe back-propagation from the field of neural networks, and GOLEM from machine learning, and illustrate their learning mechanisms using a simple expository problem. Back-propagation and GOLEM are then compared with multiple linear regression (using the parameters and their squares) on two real drug design problems: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines. © 1993 ESCOM Science Publishers B.V.
AU - King,RD
AU - Hirst,JD
AU - Sternberg,MJE
DO - 10.1007/BF02174529
EP - 290
PY - 1993///
SN - 0928-2866
SP - 279
TI - New approaches to QSAR: Neural networks and machine learning
T2 - Perspectives in Drug Discovery and Design
UR - http://dx.doi.org/10.1007/BF02174529
VL - 1
ER -
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