New approaches to QSAR: Neural networks and machine learning View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1993-12

AUTHORS

Ross D. King, Jonathan D. Hirst, Michael J. E. Sternberg

ABSTRACT

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 ofEscherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines. More... »

PAGES

279-290

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02174529

DOI

http://dx.doi.org/10.1007/bf02174529

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012761522


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