Quantitative structure-activity relationships by evolved neural networks for the inhibition of dihydrofolate reductase by pyrimidines.
Evolutionary computation provides a useful method for training neural networks in the face of multiple local optima. This paper begins with a description of methods for quantitative structure activity relationships (QSAR). An overview of artificial neural networks for pattern recognition problems such as QSAR is presented and extended with the description of how evolutionary computation can be used to evolve neural networks. Experiments are conducted to examine QSAR for the inhibition of dihydrofolate reductase by pyrimidines using evolved neural networks. Results indicate the utility of evolutionary algorithms and neural networks for the predictive task at hand. Furthermore, results that are comparable or perhaps better than those published previously were obtained using only a small fraction of the previously required degrees of freedom.
Landavazo
Dana G
DG
Natural Selection Inc., 3333 N. Torrey Pines Ct., Suite 200, 92037, La Jolla, CA 92037, USA.
Fogel
Gary B
GB
Fogel
David B
DB
D005075:Biological Evolution; D005493:Folic Acid Antagonists / Q000737:chemistry / Q000494:pharmacology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011743:Pyrimidines / Q000737:chemistry / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship; D013762:Tetrahydrofolate Dehydrogenase / Q000187:drug effects*
D005493:Folic Acid Antagonists; D011743:Pyrimidines; D013762:Tetrahydrofolate Dehydrogenase