TY - JOUR
T1 - Comparison of artificial intelligence methods for modeling pharmaceutical QSARs
AU - King, Ross D.
AU - Hirst, Jonathan D.
AU - Sternberg, Michael J E
PY - 1995/3
Y1 - 1995/3
N2 - A common step in pharmaceutical development is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure (shape) of a compound relates to its biological activity. A comparative study was carried out of six artificial intelligence and traditional algorithms for modeling QSAR's: GOLEM, CART, and M5 from symbolic machine learning; back-propagation from neural networks; and linear regression and nearest-neighbor from traditional statistics. Two test case problems were studied: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines, and the inhibition of rat/mouse tumor DHFR by triazines. It was found that there was no significant statistical difference between the methods in terms of their ability to rank unseen compounds by activity. However, symbolic machine learning methods, in particular relational ones, were found to generate rules that provided insight into the stereo-chemistry of compound receptor interactions.
AB - A common step in pharmaceutical development is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure (shape) of a compound relates to its biological activity. A comparative study was carried out of six artificial intelligence and traditional algorithms for modeling QSAR's: GOLEM, CART, and M5 from symbolic machine learning; back-propagation from neural networks; and linear regression and nearest-neighbor from traditional statistics. Two test case problems were studied: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines, and the inhibition of rat/mouse tumor DHFR by triazines. It was found that there was no significant statistical difference between the methods in terms of their ability to rank unseen compounds by activity. However, symbolic machine learning methods, in particular relational ones, were found to generate rules that provided insight into the stereo-chemistry of compound receptor interactions.
U2 - 10.1080/08839519508945474
DO - 10.1080/08839519508945474
M3 - Article
SN - 1087-6545
VL - 9
SP - 213
EP - 233
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
IS - 2
ER -
