In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint

Cao, Dong‐Sheng; Hu, Qian-Nan; Xu, Qingsong; Yang, Yan-Ning; Zhao, Jianchao; Lü, Hongmei; Zhang, Liangxiao; Liang, Yi‐Zeng

doi:10.1016/j.aca.2011.02.010

Cited by 35 publications

(19 citation statements)

References 13 publications

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“…Substructure fingerprints directly encode molecular structure in a series of binary bits that represent the presence or absence of particular substructures in the molecule. Widespread applications to SAR study, database searching and virtual screening have demonstrated the prediction ability of substructure fingerprints in various classification problems 50,51. The definition of MACCS fingerprints is available from OpenBabel (version 2.3.1, http://openbabel.org/, accessed October, 2011).…”

Section: Methodsmentioning

confidence: 99%

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Cao

Zhang

Tan

et al. 2014

Molecular Informatics

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Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

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Section: Methodsmentioning

confidence: 99%

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Cao

Zhang

Tan

et al. 2014

Molecular Informatics

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“…Molecular fingerprints have been widely applied to various chemical classification problems [38][39][40][41][42]. Although it divides the whole molecule into a large number of fragments, it has the potential to keep overall complexity of molecules.…”

Section: Datasets and Molecular Descriptionmentioning

confidence: 99%

In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines

Cao

Dong

Wang

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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“…Another approach to predict toxicity is the use of quantitative structure-toxicity relationship (QSTR) models, which relies primarily on the generation of descriptors of chemical structure and statistical analysis of the relationship between these descriptors and toxicity end point. To date, a large number of in silico toxicity models have been developed, and increasing numbers of associated papers have been published [15][16][17][18][19][20]. The literature for toxicity prediction was also recently reviewed [1,3,21].…”

Section: Introductionmentioning

confidence: 99%

Computer‐aided prediction of toxicity with substructure pattern and random forest

Cao

Yang

Zhao

et al. 2012

Journal of Chemometrics

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Toxicity of chemicals induced by different factors is an important consideration, especially during the drug research and development process. Thus, there is urgent need to develop computationally effective models that can predict the toxicity or adverse effects of chemicals for a specific class of chemicals. In this study, random forest (RF) was used to classify five toxicity data sets from Distributed Structure-Searchable Toxicity database network, using substructure fingerprints calculated directly from simple molecular structure. Three model validation approaches, out-of-bag validation incorporated in RF, fivefold cross-validation, and an independent validation set, were used for assessing the prediction capability of our models. The chemical space analysis of data sets was explored by multidimensional scaling plots, and outlying molecules were also detected by the proximity measure in RF. At the same time, the important substructure fingerprints, recognized by the RF technique, gave some insights into the structure features related to toxicity of chemicals. The results obtained showed that these in silico classification models with substructure patterns and RF are applicable for potential toxicity prediction of chemical compounds.

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In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint

Cited by 35 publications

References 13 publications

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines

Computer‐aided prediction of toxicity with substructure pattern and random forest

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