Gaussian interaction profile kernels for predicting drug–target interaction

Laarhoven, Twan van; Nabuurs, Sander B.; Marchiori, Elena

doi:10.1093/bioinformatics/btr500

Cited by 761 publications

(612 citation statements)

References 37 publications

(53 reference statements)

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“…Since then the approach has become especially popular in predicting biological interactions (see e.g. [29], [30], [3]), as well as a standard building block in more theoretical work concerning the development of multi-task learning methods (see e.g. [25], [31], [27]).…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

Airola

Pahikkala

2018

IEEE Trans. Neural Netw. Learning Syst.

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“…The interaction network uses only the chemical similarity between the drug compounds and the genomic similarity between the target proteins. Van [12] was proposed that the interaction profile be made by using a machine learning method with a binary vector to describe the presence or absence of an interaction with each goal. Interactions can be effectively used for accurate prediction of drug target interactions.…”

Section: Literature Surveymentioning

confidence: 99%

A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

B.J¹,

Joy²

2018

IJET

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In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.

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“…Following Bleakley et al [31] and Mordelet and Vert [32], Bleakley and Yamanishi [33] further proposed the bipartite local model (BLM) to predict DTIs, which used local support vector machine (SVM) classifiers with known DTIs and integrated the chemical structure similarity and protein sequence similarity information. Gaussian Interaction Profile (GIP) kernels on drug-target networks were significant improvements developed by van Laarhoven et al [34]. In order to solve the problem of negative samples, Lan et al [35] proposed a prediction method (PUDT) which classified unlabeled samples into the reliable negative examples and likely negative examples based on the similarity of protein structure and achieved good results.…”

Section: Complexitymentioning

confidence: 99%

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

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It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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Gaussian interaction profile kernels for predicting drug–target interaction

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 761 publications

References 37 publications

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

Fast Kronecker Product Kernel Methods via Generalized Vec Trick

A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

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