Navigating the kinome

Metz, James T.; Johnson, Eric F.; Soni, Niru B.; Merta, Philip J.; Kifle, Lemma; Hajduk, Philip J.

doi:10.1038/nchembio.530

Cited by 265 publications

(309 citation statements)

References 15 publications

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“…More than 150,000 inhibition data of 3,800 ligands toward 172 kinases were collected (41). To determine whether 2 kinases can be classified as related or not, mathematical tools were used.…”

Section: Biophysical and Statistical Datamentioning

confidence: 99%

Axl Kinase as a Key Target for Oncology: Focus on Small Molecule Inhibitors

Feneyrolles¹,

Spenlinhauer²,

Guiet³

et al. 2014

Molecular Cancer Therapeutics

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Receptor tyrosine kinases (RTK) are transmembrane receptors that regulate signal transduction in cells. As a member of the TAM (Tyro-3, Axl, Mer) RTK subfamily, Axl regulates key processes such as cell growth, migration, aggregation, and apoptosis through several pathways. Its overexpression/overactivation has been underlined in several conditions, especially cancers, and in both chemotherapy and targeted therapy sensitivity loss. In this review, we propose to highlight the therapeutic implication of Axl, starting with the pathways it regulates, validating its interest as a therapeutic target, and defining the tools available to develop strategies for its inhibition. We especially focus on small molecule inhibitors, their structure, inhibition profile, and development stages. Mol Cancer Ther; 13(9); 2141-8. Ó2014 AACR.

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Section: Biophysical and Statistical Datamentioning

confidence: 99%

Axl Kinase as a Key Target for Oncology: Focus on Small Molecule Inhibitors

Feneyrolles¹,

Spenlinhauer²,

Guiet³

et al. 2014

Molecular Cancer Therapeutics

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“…Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR) and Enzymes (E) datasets originally published by Yamanishi et al [24] and the Kinase (K) dataset [36]. Each of the first four datasets contains a binary interaction matrix between drugs and targets, in which each entry indicates whether the interaction between the corresponding drug and target is known or not.…”

Section: Methodsmentioning

confidence: 99%

“…First, some well-founded metrics of drugs' and targets' similarity were proposed (e.g., [24,36]) and the underlined latent space model should reflect those similarities. Furthermore, DTI prediction methods should be able to provide predictions also for drugs and targets without any known interactions.…”

Section: Modifying Bpr For Dti Predictionmentioning

confidence: 99%

Drug-target interaction prediction: A Bayesian ranking approach

Peška

Búza

Koller

2017

Computer Methods and Programs in Biomedicine

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Background and Objective: In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning -finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. Methods: We propose Bayesian Ranking Prediction of Drug-Target Interactions(BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Conclusions:Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drugcentric repositioning scenarios. BRDTI Software and supplementary materials are available online at www.ksi.mff.cuni.cz/~peska/BRDTI.

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“…From a data base of known drugs, targets and their binary interactions, the goal is to learn a model that can for new previously unseen drugs and targets predict whether they interact. We consider four drug-target interaction data sets, the GPCR, IC, E data sets [59], and the Ki data [60]. The Ki-data is a naturally sparse data set where the class information is available only for a subset of the edges.…”

mentioning

confidence: 99%

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

Airola

Pahikkala

2018

IEEE Trans. Neural Netw. Learning Syst.

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Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the training data, a setting known as zero-shot or zero-data learning. Such a setting occurs in numerous applications, including drug-target interaction prediction, collaborative filtering and information retrieval. Efficient training algorithms based on the so-called vec trick, that makes use of the special structure of the Kronecker product, are known for the case where the training data is a complete bipartite graph. In this work we generalize these results to non-complete training graphs. This allows us to derive a general framework for training Kronecker product kernel methods, as specific examples we implement Kronecker ridge regression and support vector machine algorithms. Experimental results demonstrate that the proposed approach leads to accurate models, while allowing order of magnitude improvements in training and prediction time.

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Navigating the kinome

Cited by 265 publications

References 15 publications

Axl Kinase as a Key Target for Oncology: Focus on Small Molecule Inhibitors

Axl Kinase as a Key Target for Oncology: Focus on Small Molecule Inhibitors

Drug-target interaction prediction: A Bayesian ranking approach

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

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