Genetic Programming for Combining Neural Networks for Drug Discovery

Langdon, William B.; Barrett, S. J.; Buxton, Bernard F.

doi:10.1007/978-1-4471-0123-9_51

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…One general research report [1] applied five state-of-theart algorithms, decision tree, logistic regression, k-nearestneighbor, probabilistic neural network, and support vector machine with the goal of predicting the toxicity of chemicals against an organism of Tetrahymena pyriformis. Genetic Programming, an evolutionary algorithm-based methodology, has been used to model drug bio-availability [15], [16], [17] and has been developed to predict p450 inhibition [18], [19] coupled with ANNs. Support Vector Machine has been popularly used in drug virtual screening [4], [5], [6].…”

Section: A Qsar Modelingmentioning

confidence: 99%

Predicting chemical activities from structures by attributed molecular graph classification

Xue

et al. 2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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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.

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Section: A Qsar Modelingmentioning

confidence: 99%

Predicting chemical activities from structures by attributed molecular graph classification

Xue

et al. 2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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“…There is an enormous amount of literature on the application of evolutionary algorithms (EAs) to the synthesis of artificial neural networks (NNs), this topic having been very popular for over two decades [14,24,9,2,3,29,4,18,15,19,11,22,1,7]. Research in this area, which we will term Evolutionary Neural Networks (ENNs) hereafter, can be divided in three branches.…”

Section: Evolutionary Neural Networkmentioning

confidence: 99%

Free lunches for neural network search

Poli

Graff

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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In this paper we prove that for a variety of practical situations, the no-free-lunch (NFL) theorem does not apply to algorithms that search the space of artificial neural networks, such as evolutionary algorithms. We find, in particular, that, while conditions under which NFL applies exist, these require extremely restrictive symmetries on the set of possible problems which are unlikely encountered in practice. In other words, not all algorithms are equally good at finding neural networks that solve problems under all possible performance measures: a superior search algorithm for this domain does exist.

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“…Classifier aggregation has been studied in various fields [19], [21]. For example, evolutionary computation is used for generating multiple classifiers [20], [22]. In the field of neural networks, the aggregation of multiple classifiers is often referred to as "mixture of local experts [17], [18]".…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

Ishibuchi

Yamamoto

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we propose an idea of using EMO algorithms for constructing an ensemble of fuzzy rule-based classifiers with high diversity. The classification of new patterns is performed based on the vote of multiple classifiers generated by a single run of EMO algorithms. Even when the classification performance of individual classifiers is not high, their ensemble often works well. The point is to generate multiple classifiers with high diversity. We demonstrate the ability of EMO algorithms to generate various non-dominated fuzzy rule-based classifiers with high diversity by their single run. Through computational experiments on some wellknown benchmark data sets, it is shown that the vote of generated fuzzy rulebased classifiers leads to high classification performance on test patterns.

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Genetic Programming for Combining Neural Networks for Drug Discovery

Cited by 15 publications

References 8 publications

Predicting chemical activities from structures by attributed molecular graph classification

Predicting chemical activities from structures by attributed molecular graph classification

Free lunches for neural network search

Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

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