Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity

Polishchuk, Pavel; Muratov, Eugene; Artemenko, Anatoly G.; Kolumbin, Oleg G.; Muratov, N. N.; Кузьмин, В. Е.

doi:10.1021/ci900203n

Cited by 146 publications

(95 citation statements)

References 40 publications

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“…[40,45] The tree was built in the space of the predictions of 50 single models using Kruskal's algorithm. [46] Then average distance (d av ) and its root-mean-square deviation (s) among all tree edges were calculated.…”

Section: Modeling Techniques and Applicability Domainmentioning

confidence: 99%

Universal Approach for Structural Interpretation of QSAR/QSPR Models

Polishchuk

Кузьмин

Artemenko

et al. 2013

Molecular Informatics

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In this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied. This universality was achieved by using only the information obtained from substructures of the compounds of interest to interpret model outcomes. Reliability of the offered approach was confirmed by the results of three case studies, including end-points of different types (continuous and binary classification) and nature (solubility, mutagenicity, and inhibition of Transglutaminase 2), various fragment and whole-molecule descriptors (Simplex and Dragon), and multiple modeling techniques (partial least squares, random forest, and support vector machines). We compared the global contributions of molecular fragments obtained using our methodology with known SAR rules derived experimentally. In all cases high concordance between our interpretation and results published by others was observed. Although the proposed interpretation approach could be easily extended to any type of descriptors, we would recommend using Simplex descriptors to achieve a larger variety of investigated molecular fragments. The developed approach is a good tool for interpretation of such "black box" models like random forest, neural networks, etc. Analysis of fragment global contributions and their deviation across a dataset could be useful for the identification of key fragments and structural alerts. This information could be helpful to maximize the positive influence of structural surroundings on the given fragment and to decrease the negative effects.

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Section: Modeling Techniques and Applicability Domainmentioning

confidence: 99%

Universal Approach for Structural Interpretation of QSAR/QSPR Models

Polishchuk

Кузьмин

Artemenko

et al. 2013

Molecular Informatics

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“…In contrast to similarity based methods, feature based methods either select input features (chemical descriptors) or weight them by a score or a model parameter. Feature-based approaches include (generalized) linear models (e.g., Luco and Ferretti, 1997;Sagardia et al, 2013), random forests, (e.g., Svetnik et al, 2003;Polishchuk et al, 2009), and scoring schemes based on naive Bayes (Bender et al, 2004;Xia et al, 2004). Choosing informative features for the task at hand is key in feature-based methods and requires deep insights into chemical and biological properties and processes (Verbist et al, 2015), such as interactions between molecules (e.g., ligand-target), reactions and enzymes involved, and metabolic modifications of the molecules.…”

Section: Introductionmentioning

confidence: 99%

DeepTox: Toxicity Prediction using Deep Learning

Mayr

Klambauer

Unterthiner

et al. 2016

Front. Environ. Sci.

717

682

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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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“…21 The UNC group used the sphere exclusion rational design method 14 and the random forest (Breiman 2001) and k Nearest Neighbors (kNN) (Zheng and Tropsha, 2000) QSAR methods. Additionally, the minimal test set dissimilarity 22 and random forest approach 23 were used by the UNC team in collaboration with A.V. Bogatsky Physical Chemical Institute NASU (UNC2).…”

Section: Introductionmentioning

confidence: 99%

Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

Martin

Harten

Young

et al. 2012

J. Chem. Inf. Model.

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238

161

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Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

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Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity

Cited by 146 publications

References 40 publications

Universal Approach for Structural Interpretation of QSAR/QSPR Models

Universal Approach for Structural Interpretation of QSAR/QSPR Models

DeepTox: Toxicity Prediction using Deep Learning

Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

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