Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

Nidhi, -; Glick, Meir; Davies, John W.; Jenkins, Jeremy L.

doi:10.1021/ci060003g

Cited by 306 publications

(126 citation statements)

References 47 publications

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“…Naïve Bayes is a commonly applied stochastic classifier based on the Bayes theorem of conditional probability (Nidhi et al, 2006). The major characteristic of the classifier is the naïve assumption that all input features are independent.…”

Section: Naïve Bayes Learningmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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Citation:Drwal MN, Siramshetty VB, Banerjee P, Goede A, Preissner R and Dunkel M (2015) To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modeling general and complex endpoints to the investigation and modeling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the twenty-first century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modeled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.

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Section: Naïve Bayes Learningmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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“…Bayesian classification has been applied in many studies and was recently compared with other machine-learning techniques. [18][19][20][21] To classify promiscuous from selective compounds, we used the Bayesian modeling protocol available in Pipeline Pilot (SciTegic). [26] A large number of models were built using different sets of descriptors.…”

Section: Naïve Bayesian (Nb) Modelingmentioning

confidence: 99%

Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data

et al. 2007

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This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.

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“…However Bayesian analysis appears notably absent in routine published practice (Klon, 2009;Nidhi et al, 2006). Computational limitations seem less likely than the poor understanding surrounding the use of these methods.…”

Section: How Actionable Is Your Approach?mentioning

confidence: 99%

High Content and Throughput Drug Discovery

Wills¹

2011

Systems and Computational Biology - Molecular and Cellular Experimental Systems

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Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

Cited by 306 publications

References 47 publications

Molecular similarity-based predictions of the Tox21 screening outcome

Molecular similarity-based predictions of the Tox21 screening outcome

Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data

High Content and Throughput Drug Discovery

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