Pred‐hERG: A Novel web‐Accessible Computational Tool for Predicting Cardiac Toxicity

Braga, Rodolpho C.; Alves, Vinícius M.; Silva, Meryck F. B.; Muratov, Eugene; Fourches, Denis; Lião, Luciano M.; Tropsha, Alexander; Andrade, Carolina Horta

doi:10.1002/minf.201500040

Cited by 183 publications

(144 citation statements)

References 21 publications

Supporting

Mentioning

136

Contrasting

Order By: Relevance

“…[10, 35] The training sets vary from tens to a few thousands of compounds, where large datasets are either compilation of literature data, such as ChEMBL, [13b, 36] or corporate datasets off the public domain. [8, 37] In this study, we presented a 3,024 non-redundant hERG dataset, consisting of the marketed drugs, drug candidates reaching the clinical trials, and other bioactive compounds.…”

Section: Resultsmentioning

confidence: 99%

“…Most of QSAR models published so far are either based on small training sets containing tens to hundreds of compounds, [12] or trained on larger data sets compiled from multiple sources, such as those from the CHEMBL. [13] Since data quality and data integrity determine the performance of QSAR models, it is not recommended to compile datasets from different laboratories, especially for in vivo and in vitro data, such as the hERG activity data. [14] For example, large variations in hERG potencies have been reported for the same compound measured by using different cell lines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Sun

Huang

Xia

et al. 2016

Molecular Informatics

View full text Add to dashboard Cite

Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-a-go-go-related gene (hERG) encoded potassium ion (K+) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Sun

Huang

Xia

et al. 2016

Molecular Informatics

View full text Add to dashboard Cite

show abstract

“…In addition, we removed compounds with previous bioactivity data reported against Sm TGR or S. mansoni and pan-assay interference compounds (PAINs) 59,60 so that selected compounds would be novel Sm TGR inhibitors and contain no PAINs structures. Finally, the compounds were evaluated by predicting a panel of properties including high aqueous solubility (CIQPlogS), 61 acceptable binding to human serum albumin (QPlogKhsa), 61 acceptable brain/blood partition coefficient (QPlogBB), 61 nonblocking or weak blocking of hERG channel, 46,47 and absence of carcinogenicity and hepatotoxicity. 32 At the end of the VS workflow, 29 putative hits were visually inspected and acquired for biological evaluation (Supporting Information, Table S6).…”

Section: Resultsmentioning

confidence: 99%

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

et al. 2016

Self Cite

View full text Add to dashboard Cite

Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.

show abstract

“…17 Scientific applications implemented in a web interface (web apps) are an interesting alternative to standalone software applications, since they are ready to use, fast, and they usually present an intuitive interface. Several web apps to solve chemical and molecular modeling problems have been proposed through the years, such as the Platform for Unified Molecular Analysis (PUMA), 18 SWISS-MODEL, 19,20 Chembench, 21 Pred-hERG, 22,23 Pred-Skin, 24 among others. Conversely, to a lesser extent, chemistry mobile apps have also been developed and they tend to see a rapid uptake in the next few years since smartphones and tablet computers are becoming ubiquitous.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, our group has developed and implemented predictive QSAR models in two distinctive tools available as a web app (Pred-hERG) 23 and web and mobile apps (Pred-Skin), 24 for the fast prediction of hERG cardiac toxicity and skin sensitization, respectively. Here, we aim to describe the details behind the development of these two innovative and impactful web-based and mobile applications for chemical toxicity prediction.…”

Section: Introductionmentioning

confidence: 99%

Development of Web and Mobile Applications for Chemical Toxicity Prediction

Alves¹,

Braga²,

Muratov³

et al. 2018

J. Braz. Chem. Soc.

Self Cite

View full text Add to dashboard Cite

Computational tools are recognized to provide high-quality predictions for the assessment of chemical toxicity. In the recent years, mobile devices have become ubiquitous, allowing for the development of innovative and useful models implemented as chemical software applications. Here, we will briefly discuss this recent uptick in the development of web-based and mobile applications for chemical problems, focusing on best practices, development, usage and interpretation. As an example, we also describe two innovative apps (Pred-hERG and Pred-Skin) for chemical toxicity prediction developed in our laboratory. These applications are based on predictive quantitative structure-activity relationships (QSAR) models developed using the largest publicly available datasets of structurally diverse compounds. The developed tools ensure both highly accurate predictions and easy interpretation of the models, allowing users to discriminate potential toxicants and to purpose structural modifications to design safer chemicals.

show abstract

Pred‐hERG: A Novel web‐Accessible Computational Tool for Predicting Cardiac Toxicity

Cited by 183 publications

References 21 publications

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Development of Web and Mobile Applications for Chemical Toxicity Prediction

Contact Info

Product

Resources

About