Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives

Darnag, Rachid; Mazouz, E.; Schmitzer, Andreea R.; Villemin, Didier; Jarid, Abdellah; Cherqaoui, Driss

doi:10.1016/j.ejmech.2010.01.002

Cited by 61 publications

(35 citation statements)

References 37 publications

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“…Similarity-based methods compute a matrix of pairwise similarities between compounds which is subsequently used by the prediction algorithms. These methods, which are based on the idea that similar compounds should have a similar biological effect include nearest neighbor algorithms (e.g., Kauffman and Jurs, 2001;Ajmani et al, 2006;Cao et al, 2012) and support vector machines (SVMs, e.g., Mahé et al, 2005;Niu et al, 2007;Darnag et al, 2010). SVMs rely on a kernel matrix which represents the pairwise similarities of objects.…”

Section: Introductionmentioning

confidence: 99%

DeepTox: Toxicity Prediction using Deep Learning

Mayr

Klambauer

Unterthiner

et al. 2016

Front. Environ. Sci.

720

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: Introductionmentioning

confidence: 99%

DeepTox: Toxicity Prediction using Deep Learning

Mayr

Klambauer

Unterthiner

et al. 2016

Front. Environ. Sci.

720

697

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“…In order to evaluate the predictive performance of this model, some other investigations were used for , ARD, and AAE in this work were higher than those of the works of Darnag et al 5 and Huuskonen's. 15 To sum up, our linear model was accurate.…”

Section: Resultsmentioning

confidence: 96%

A QSAR Model for Predicting the Anti HIV‐1 Activity of TIBO Derivatives Using the Norm Indexes

Yan

et al. 2016

Bulletin Korean Chem Soc

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“…The advantage of such kernel methods is that they directly give the instance similarity within that mapped space without asking the users to explicitly design such space or understanding the structure of the space. SVMs achieve the stated-of-the-art performance among current classification methods in many application domains, and for SAR model learning, they are also the most popular and successful options [Darnag et al, 2010;Byvatov et al, 2003;Deshpande et al, 2005;Wale et al, 2007].…”

Section: Machine Learning Algorithms For Conventional In Silico Modelsmentioning

confidence: 99%

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

Ning

Karypis

2010

Drug Development Research

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In this article, we review the recent development for in silico Structure-Activity-Relationship (SAR) models using machine-learning techniques. The review focuses on the following topics: machine-learning algorithms for computational SAR models, single-target-oriented SAR methodologies, Chemogenomics, and future trends. We try to provide the state-of-the-art SAR methods as well as the most up-to-date advancement, in order for the researchers to have a general overview at this area. Drug Dev Res 72:138-146, 2011.

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Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives

Cited by 61 publications

References 37 publications

DeepTox: Toxicity Prediction using Deep Learning

DeepTox: Toxicity Prediction using Deep Learning

A QSAR Model for Predicting the Anti HIV‐1 Activity of TIBO Derivatives Using the Norm Indexes

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

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