RADER: a RApid DEcoy Retriever to facilitate decoy based assessment of virtual screening

Pang, Xiaoqian; Li, Yecheng; Zhang, Ziying; Tan, Wenan

doi:10.1093/bioinformatics/btw783

Cited by 27 publications

(20 citation statements)

References 10 publications

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“…After that, triple corresponding decoys were generated in RApid DEcoy Retriever (RADER) online database with a similarity threshold value of 0.75 between liver toxic compounds and decoys (Ling et al, 2016). Only molecules with molecular weight between 100 and 800 were preserved.…”

Section: Methodsmentioning

confidence: 99%

“…We considered the liver toxic compounds as hepatotoxic (labeled as “1”) during the calculation. Since decoys were randomly selected from a massive “decoy pool” containing 70,030,298 compounds (Ling et al, 2016) after eliminating hepatotoxic compounds, decoys were supposed as non-hepatotoxic (labeled as “0”) in this study. Eventually, the training set contained 453 hepatotoxic compounds and 1,359 non-hepatotoxic compounds, while the test set was consisted of 166 hepatotoxic compounds and 498 non-hepatotoxic compounds.…”

Section: Methodsmentioning

confidence: 99%

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In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

Cai

Guo

et al. 2019

Front. Pharmacol.

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Backgrounds and Aims Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI. Materials and Methods In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM. Results 16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity. Conclusion Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

Cai

Guo

et al. 2019

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…The robustness of the predictive model was also checked by decoy set using RApid DEcoy Retriever (RADER) software (Wang et al, 2017). The decoy set are the chemicals with similar H-bond donor, molecular weight, and logP but chemically dissimilar than active molcules (Rajput and Kumar, 2018).…”

Section: Methodsmentioning

confidence: 99%

Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus

Rajput

Kumar

2019

Front. Pharmacol.

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Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “ anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC 50 ). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/ .

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“…We used decoy set to check the robustness of our developed models. There were few tools like DUD (Huang et al, 2006), DecoyFinder (Cereto-Massague et al, 2012), and RADER (Wang et al, 2017) for designing the decoys of the chemicals. In our study, the decoys were generated from the latest tool, i.e., RApid DEcoy Retriever (RADER) software (Wang et al, 2017) against the 2168 anti-flavi chemicals with similar 1D physicochemical properties but different 2D topology.…”

Section: Methodsmentioning

confidence: 99%

Anti-flavi: A Web Platform to Predict Inhibitors of Flaviviruses Using QSAR and Peptidomimetic Approaches

Rajput

Kumar

2018

Front. Microbiol.

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Flaviviruses are arboviruses, which comprises more than 70 viruses, covering broad geographic ranges, and responsible for significant mortality and morbidity globally. Due to the lack of efficient inhibitors targeting flaviviruses, the designing of novel and efficient anti-flavi agents is an important problem. Therefore, in the current study, we have developed a dedicated prediction algorithm anti-flavi, to identify inhibition ability of chemicals and peptides against flaviviruses through quantitative structure–activity relationship based method. We extracted the non-redundant 2168 chemicals and 117 peptides from ChEMBL and AVPpred databases, respectively, with reported IC50 values. The regression based model developed on training/testing datasets of 1952 chemicals and 105 peptides displayed the Pearson’s correlation coefficient (PCC) of 0.87, 0.84, and 0.87, 0.83 using support vector machine and random forest techniques correspondingly. We also explored the peptidomimetics approach, in which the most contributing descriptors of peptides were used to identify chemicals having anti-flavi potential. Conversely, the selected descriptors of chemicals performed well to predict anti-flavi peptides. Moreover, the developed model proved to be highly robust while checked through various approaches like independent validation and decoy datasets. We hope that our web server would prove a useful tool to predict and design the efficient anti-flavi agents. The anti-flavi webserver is freely available at URL http://bioinfo.imtech.res.in/manojk/antiflavi.

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RADER: a RApid DEcoy Retriever to facilitate decoy based assessment of virtual screening

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 27 publications

References 10 publications

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus

Anti-flavi: A Web Platform to Predict Inhibitors of Flaviviruses Using QSAR and Peptidomimetic Approaches

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