A computational approach to finding novel targets for existing drugs.
TLDR
A computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions.Abstract:
Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.read more
Citations
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Journal ArticleDOI
A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
Hua Yu,Jianxin Chen,Xue Xu,Yan Li,Huihui Zhao,Yupeng Fang,Xiuxiu Li,Wei Zhou,Wei Wang,Yonghua Wang +9 more
TL;DR: This work reports a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest and Support Vector Machine, and demonstrates the reliability and robustness of the obtained models.
Journal ArticleDOI
Toward more realistic drug–target interaction predictions
Tapio Pahikkala,Antti Airola,Sami Pietilä,Sushil Kumar Shakyawar,Agnieszka Szwajda,Jing Tang,Tero Aittokallio +6 more
TL;DR: In this paper, the effects of four factors that may lead to dramatic differences in the prediction results are investigated: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither).
Journal ArticleDOI
A Drug Repositioning Approach Identifies Tricyclic Antidepressants as Inhibitors of Small Cell Lung Cancer and Other Neuroendocrine Tumors
Nadine Jahchan,Joel T. Dudley,Pawel K. Mazur,Natasha M. Flores,Dian Yang,Alec Palmerton,Anne-Flore Zmoos,Dedeepya Vaka,Kim Q.t. Tran,Margaret Zhou,Karolina Krasinska,Jonathan W. Riess,Joel W. Neal,Purvesh Khatri,Kwon S. Park,Atul J. Butte,Julien Sage +16 more
TL;DR: This work shows the power of bioinformatics-based drug approaches to rapidly repurpose FDA-approved drugs and identifies a novel class of molecules to treat patients with SCLC, a cancer for which no effective novel systemic treatments have been identified in several decades.
Journal ArticleDOI
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.
TL;DR: The proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively.
Journal ArticleDOI
SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines
TL;DR: A method is presented called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions and outperform the previously reported models across the studied datasets.
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