NDLI: Predicting chemical activities from structures by attributed molecular graph classification

Content Provider	IEEE Xplore Digital Library
Author	Qian Xu Derek Hao Hu Hong Xue Qiang Yang
Copyright Year	2010
Description	Author affiliation: Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong, China (Qian Xu; Derek Hao Hu; Hong Xue; Qiang Yang)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	536888
Page Count	8
File Format	PDF
ISBN	9781424467662
e-ISBN	9781424467662
DOI	10.1109/CIBCB.2010.5510690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Chemical compounds Support vector machines Drugs Biology computing Training data Machine learning algorithms Decision trees Logistics Regression tree analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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