Predicting chemical activities from structures by attributed molecular graph classification

• Published in: 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology pp. 1 - 8
• Main Authors: Qian Xu, Derek Hao Hu, Hong Xue, Qiang Yang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2010
• Subjects: Biology computing; Chemical compounds; Decision trees; Drugs; Kernel; Logistics; Machine learning algorithms; Regression tree analysis; Support vector machines; Training data
• ISBN: 9781424467662; 1424467667
• DOI: 10.1109/CIBCB.2010.5510690

Designing Quantitative Structure-Activity Relationship (QSAR) models has been a recurrent research interest for biologists and computer scientists. An example is to predict the toxicity of chemical compounds using their structural properties as features represented by graphs. A popular method to classify these graphs is to exploit classifiers such as support vector machines (SVMs) and graph kernels to incorporate the sequential, structural and chemical information. Previous works have focused on designing specific graph kernels for this task, amongst which graph alignment kernels are one of the most popular approach. Graph alignment kernels align the nodes of one graph to the nodes of the second graph so that the total overall similarity is maximized with respect to all possible alignments. However, taking both vertex and edge similarities into account makes the problem NP-Hard. In this paper, we present a novel general graph-matching based method for QSAR. We view the problem of calculating optimal assignments of two attributed graphs from a different perspective. Instead of first designing an atom kernel function and a bond kernel function, we first provide a training set of pairs of graphs with their corresponding matchings. We then try to learn the compatibility function over atoms and use only the atom kernel function to compute graph matchings. Our algorithm has the advantage of being more general and yet efficient than previous approaches for the QSAR problem. We evaluate our method on a set of chemical structure-activity prediction benchmark datasets, and show that our algorithm can achieve better or comparable accuracies over the optimal assignment kernel method.