Graph wavelet alignment kernels for drug virtual screening.
In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.
Smalter
Aaron
A
Department of Electrical Engineering and Computer Science, 1520 West 15th Street, University of Kansas, Lawrence, KS 66045, USA. asmalter@ku.edu
Huan
Jun
J
Lushington
Gerald
G
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003196:Computer Graphics; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data*; D008956:Models, Chemical; D015233:Models, Statistical; D013329:Structure-Activity Relationship; D014584:User-Computer Interface