A maximum common subgraph kernel method for predicting the chromosome aberration test.

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.

Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

Discrimination networks for maximum selection.

We construct a novel discrimination network using differentiating units for maximum selection. In contrast to traditional competitive architectures like MAXNET the discrimination network does not only signal the winning unit, but also provides information about its evidence. In particular, we show that a discrimination network converges to a stable state within finite time and derive three characteristics: intensity normalization (P1), contrast enhancement (P2), and evidential response (P3). In order to improve the accuracy of the evidential response we incorporate distributed redundancy into the network. This leads to a system which is not only robust against failure of single units and noisy data, but also enables us to sharpen the focus on the problem given in terms of a more accurate evidential response. The proposed discrimination network can be regarded as a connectionist model for competitive learning by evidence.