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

Mohr, Johannes; Jain, Brijnesh J.; Sutter, Andreas; Laak, Antonius ter; Steger‐Hartmann, Thomas; Heinrich, Nikolaus; Obermayer, Klaus

doi:10.1021/ci900367j

Cited by 19 publications

(34 citation statements)

References 60 publications

(125 reference statements)

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“…They are widely used in chemoinformatics (Mohr et al, 2010;Rosenbaum et al, 2011). SVMs are similarity-based machine learning methods and therefore depend on a kernel function that determines the similarity of two compounds.…”

Section: Support Vector Machinesmentioning

confidence: 99%

DeepTox: Toxicity Prediction using Deep Learning

Mayr

Klambauer

Unterthiner

et al. 2016

Front. Environ. Sci.

767

697

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The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams "Bioinf@JKU"). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.

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Section: Support Vector Machinesmentioning

confidence: 99%

DeepTox: Toxicity Prediction using Deep Learning

Mayr

Klambauer

Unterthiner

et al. 2016

Front. Environ. Sci.

767

697

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“…Kernel methods have also been extensively applied to Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) problems [2, 5, 6] whose purposes are to predict the chemical activity and property for a given chemical compound, respectively. For example, various kernel functions for QSAR/QSPR have been developed based on alignment of two chemical graphs [33, 34], three-dimensional superposition [35], Tanimoto and other coefficients [36], molecular descriptors [37], and subtree patterns [38]. …”

Section: Kernel Methods and Pre-image Problemmentioning

confidence: 99%

Comparison and Enumeration of Chemical Graphs

Akutsu

Nagamochi

2013

Computational and Structural Biotechnology Journal

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Chemical compounds are usually represented as graph structured data in computers. In this review article, we overview several graph classes relevant to chemical compounds and the computational complexities of several fundamental problems for these graph classes. In particular, we consider the following problems: determining whether two chemical graphs are identical, determining whether one input chemical graph is a part of the other input chemical graph, finding a maximum common part of two input graphs, finding a reaction atom mapping, enumerating possible chemical graphs, and enumerating stereoisomers. We also discuss the relationship between the fifth problem and kernel functions for chemical compounds.

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“…Third, we employed the CA data set compiled by Mohr et al [6]. This data set consists of 351 positive and 589 negative compounds with respect to the chromosome aberration test.…”

Section: Methodsmentioning

confidence: 99%

“…Swamidass et al [5] introduced the Influence Relevance Voter, an interpretable method based on a supervised artificial neural network in combination with a k-nearest neighbor approach. Recently, Mohr et al [6] employed a potential support vector machine (SVM) in combination with a maximum-common subgraph kernel to predict the genotoxicity of a compound.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interpreting linear support vector machine models with heat map molecule coloring

et al. 2011

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BackgroundModel-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.ResultsWe evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.ConclusionsIn combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.

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

Cited by 19 publications

References 60 publications

DeepTox: Toxicity Prediction using Deep Learning

DeepTox: Toxicity Prediction using Deep Learning

Comparison and Enumeration of Chemical Graphs

Interpreting linear support vector machine models with heat map molecule coloring

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