Summary
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.
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King, R.D., Hirst, J.D. & Sternberg, M.J.E. New approaches to QSAR: Neural networks and machine learning. Perspectives in Drug Discovery and Design 1, 279–290 (1993). https://doi.org/10.1007/BF02174529
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DOI: https://doi.org/10.1007/BF02174529