A Maximum Common Subgraph Kernel Method for Predicting the Chromosome Aberration Test

• Published in: Journal of chemical information and modeling Vol. 50; no. 10; pp. 1821 - 1838
• Main Authors: Mohr, Johannes, Jain, Brijnesh, Sutter, Andreas, Laak, Antonius Ter, Steger-Hartmann, Thomas, Heinrich, Nikolaus, Obermayer, Klaus
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 25.10.2010
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial Intelligence; Biological and medical sciences; Cells; Chromosome Aberrations - chemically induced; Chromosomes; Computer science; control theory; systems; Data processing. List processing. Character string processing; Drug-Related Side Effects and Adverse Reactions; Exact sciences and technology; General pharmacology; Information retrieval. Graph; Mammals; Medical sciences; Memory organisation. Data processing; Models, Genetic; Models, Molecular; Mutagenicity Tests - methods; Mutagens - adverse effects; Mutagens - chemistry; Pharmaceutical Modeling; Pharmaceutical Preparations - chemistry; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Software; Theoretical computing; Drug; Statistical analysis; Early phase; High precision; Very large databases; Functional analysis; Experimental study; In vitro; Modeling; Optimization; Kernel method; Guidance; Screening; Genetic algorithm; Vector support machine; Subgraph; Genetics; Graph method; Reliability; Pharmaceutical industry; Damaging
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/ci900367j

The chromosome aberration test is frequently used for the assessment of the potential of chemicals and drugs to elicit genetic damage in mammalian cells in vitro. Due to the limitations of experimental genotoxicity testing in early drug discovery phases, a model to predict the chromosome aberration test yielding high accuracy and providing guidance for structure optimization is urgently needed. In this paper, we describe a machine learning approach for predicting the outcome of this assay based on the structure of the investigated compound. The novelty of the proposed method consists in combining a maximum common subgraph kernel for measuring the similarity of two chemical graphs with the potential support vector machine for classification. In contrast to standard support vector machine classifiers, the proposed approach does not provide a black box model but rather allows to visualize structural elements with high positive or negative contribution to the class decision. In order to compare the performance of different methods for predicting the outcome of the chromosome aberration test, we compiled a large data set exhibiting high quality, reliability, and consistency from public sources and configured a fixed cross-validation protocol, which we make publicly available. In a comparison to standard methods currently used in pharmaceutical industry as well as to other graph kernel approaches, the proposed method achieved significantly better performance.