SuperPred: update on drug classification and target prediction

Nickel, Janette; Gohlke, Björn-Oliver; Erehman, Jevgeni; Banerjee, Priyanka; Rong, Wen Wei; Goede, Andrean; Dunkel, Mathias; Preißner, Robert

doi:10.1093/nar/gku477

Cited by 339 publications

(215 citation statements)

References 31 publications

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“…Unlike most available ligand-based prediction methods [12][13][14][15][16][17], the accuracy of our approach does not rely on chemical similarity between compounds in the training/test sets. For instance, our screen against CHIP, a target with no known small molecule inhibitors, delivered four out of six binding compounds, whereas a parallel analysis using a state-of-the-art structure-based virtual screening [38,60] yielded only two weak-binding compounds.…”

Section: Discussionmentioning

confidence: 99%

Predicting protein targets for drug-like compounds using transcriptomics

Pabon

Yan

Estabrooks

et al. 2018

Preprint

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SummarySystems biology seeks to understand how normal and disease protein networks respond when specific interactions are disrupted. A first step towards this goal is identifying the molecular target(s) of bioactive compounds. Here, we hypothesize that inhibitory drugs should produce network-level effects similar to silencing the inhibited gene and show that drug-protein interactions are encoded in mRNA expression profile correlations. We use machine learning to classify correlations between drug-and knockdown-induced expression signatures and enrich our predictions through a structure-based screen.Interactions manifest both as direct correlations between drug and target knockdowns, and as indirect correlations with up/downstream knockdowns. Cross-validation on 152 FDA-approved drugs and 3104 potential targets achieved top 10/100 prediction accuracies of 26/41%. We apply our method to 1680 bioactive compounds and experimentally validate five previously unknown interactions. Our pipeline can accelerate drug discovery by matching existing compounds to new therapeutic targets while informing on network and multi-target effects.

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Section: Discussionmentioning

confidence: 99%

Predicting protein targets for drug-like compounds using transcriptomics

Pabon

Yan

Estabrooks

et al. 2018

Preprint

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“…For prediction of various therapeutic potential of these molecules, commercially and publicly available technologies as below were utilized.PharmaExpert (http://www.pharmaexpert.ru)—PASS [18]Superpred (http://prediction.charite.de)—Predictive Targets [19]SwissTargetPrediction (http://www.swisstargetprediction.ch)—Predictive Target [20]CDRUG (http://bsb.kiz.ac.cn/CDRUG)—Anti-cancer activity [21] …”

Section: Methodsmentioning

confidence: 99%

Cheminformatics studies to analyze the therapeutic potential of phytochemicals from Rhazya stricta

Obaid

Voleti²,

Bora

et al. 2017

Chemistry Central Journal

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Rhazya stricta is a unique medicinal plant source for many indole alkaloids, non-alkaloids, flavonoids, triterpenes and other unknown molecules with tremendous potential for therapeutic applications against many diseases. In the present article, we generated computational data on predictive properties and activity across two key therapeutic areas of cancer and obesity, and corresponding cheminformatics studies were carried out to examine druggable properties of these alkaloids. Computed physiochemical properties of the 78 indole alkaloids from R. stricta plant using industry-standard scientific molecular modeling software and their predictive anti-cancer activities from reliable web-source technologies indicate their plausible therapeutic applications. Their predictive ADME properties are further indicative of their drug-like-ness. We believe that the top-ranked molecules with anti-cancer activity are clearly amenable to chemical modifications for creating potent, safe and efficacious compounds with the feasibility of generating new chemical entities after pre-clinical and clinical studies.

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“…Computational predictions often rely on the observation or assumption that similar molecules manifest a similar biological effect. Similaritybased methods have been successfully applied to solve various research questions including predictions of targets (Campillos et al, 2008), therapeutic indications (Nickel et al, 2014) or side-effects (Lounkine et al, 2012). In particular, machine learning approaches such as k-nearest neighbors, naïve Bayes Abbreviations: 2D, two-dimensional; AhR, aryl hydrocarbon receptor; AR, androgen receptor; ARE, antioxidant response element; ATAD5, genotoxicity induction; AUC, area under the curve; BAC, balanced accuracy; ER, estrogen receptor 1; HSE, heat shock response; LBD, ligand binding domain; MMP, mitochondrial membrane potential; PPAR, peroxisome proliferator-activated receptor; ROC, receiver operating characteristic; Tox21, U.S. Toxicology in the twenty-first century initiative.…”

Section: Introductionmentioning

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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SuperPred: update on drug classification and target prediction

Cited by 339 publications

References 31 publications

Predicting protein targets for drug-like compounds using transcriptomics

Predicting protein targets for drug-like compounds using transcriptomics

Cheminformatics studies to analyze the therapeutic potential of phytochemicals from Rhazya stricta

Molecular similarity-based predictions of the Tox21 screening outcome

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