Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression

Búza, Krisztián; Peška, Ladislav

doi:10.1016/j.neucom.2017.04.055

Cited by 42 publications

(27 citation statements)

References 51 publications

(57 reference statements)

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“…[1][2] Drug-Target Interaction (DTI) predictions can be performed using the experimental (in-vivo) methods or computational (in silico) methods. Computational methods are broadly classified into ligand-based approach, [3] molecular docking /structurebased approach, [4] text mining, [5] based on gene ontology, [6] chemo-genomic approach, [7] [20] network-based methods, [8,21,23] learning-based, [9][10][11][12][13][14][15][16][17]22] and others. [18][19] These available resources are data driven and are proved to be useful to address and develop a new predictor for DTIs.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

Redkar

Mondal

Joseph

et al. 2020

Molecular Informatics

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Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and drugs data makes it difficult to identify the new associations between drugs and targets. Therefore, we use computational methods as it helps in accelerating the DTI identification process. Usually, available data driven sources consisting of known DTI is used to train the classifier to predict the new DTIs. Such datasets often face the problem of class imbalance. Therefore, in this study we address two challenges faced by such datasets, i. e., class imbalance and high dimensionality to develop a predictive model for DTI prediction. The study is carried out on four protein classes namely Enzyme, Ion Channel, G Protein-Coupled Receptor (GPCR) and Nuclear Receptor. We encoded the target protein sequence using the dipeptide composition and drug with a molecular descriptor. A machine learning approach is employed to predict the DTI using wrapper feature selection and synthetic minority oversampling technique (SMOTE). The ensemble approach achieved at the best an accuracy of 95.9 %, 93.4 %, 90.8 % and 90.6 % and 96.3 %, 92.8 %, 90.1 %, and 90.2 % of precision on Enzyme, Ion Channel, GPCR and Nuclear Receptor datasets, respectively, when evaluated excluding SMOTE samples with 10-fold cross validation. Furthermore, our method could predict new drug-target interactions not contained in training dataset. Selected features using wrapper feature selection may be important to understand the DTI for the protein categories under this study. Based on our evaluation, the proposed method can be used for understanding and identifying new drug-target interactions. We provide the readers with a standalone package available at https://github.com/shwetagithub1/ predDTI which will be able to provide the DTI predictions to user for new query DTI pairs.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

Redkar

Mondal

Joseph

et al. 2020

Molecular Informatics

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“…The prediction provided by the model in both cases is merged to generate the final interactions. Many other techniques such as [31][32][33] fall under this generic approach.…”

Section: Introductionmentioning

confidence: 99%

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Mongia

Majumdar

2019

Preprint

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Drug discovery is an important field in the pharmaceutical industry with one of its crucial chemogenomic process being drug-target interaction prediction. This interaction determination is expensive and laborious, which brings the need for alternative computational approaches which could help reduce the search space for biological experiments. This paper proposes a novel framework for drug-target interaction (DTI) prediction: Multi-Graph Regularized Deep Matrix Factorization (MGRDMF). The proposed method, motivated by the success of deep learning, finds a low-rank solution which is structured by the proximities of drugs and targets (drug similarities and target similarities) using deep matrix factorization. Deep matrix factorization is capable of learning deep representations of drugs and targets for interaction prediction. It is an established fact that drug and target similarities incorporation preserves the local geometries of the data in original space and learns the data manifold better. However, there is no literature on which the type of similarity matrix (apart from the standard biological chemical structure similarity for drugs and genomic sequence similarity for targets) could best help in DTI prediction. Therefore, we attempt to take into account various types of similarities between drugs/targets as multiple graph Laplacian regularization terms which take into account the neighborhood information between drugs/targets. This is the first work which has leveraged multiple similarity/neighborhood information into the deep learning framework for drug-target interaction prediction. The cross-validation results on four benchmark data sets validate the efficacy of the proposed algorithm by outperforming shallow state-of-the-art computational methods on the grounds of AUPR and AUC.September 18, 2019 1/17 interaction not only assists drug discovery but also affects other fields such as drug repositioning, drug resistance and side-effect prediction [10]. As an example, Drug repositioning [11,12] (using an existing drug for new indications) can grant polypharmacology (multi-target effect) to a drug. Gleevec (imatinib mesylate) is one of the many such examples which was successfully repositioned. Earlier, it was known to interact only with the Bcr-Abl fusion gene which is indicative of leukemia. However, later discoveries showing that it also interacts with PDGF and KIT, repositioned it for the treatment of gastrointestinal stromal tumors [13,14]. The methods available for prediction of DTI can be divided into the following three broad categories: Ligand-based approaches, Docking based approaches, and Chemogenomic approaches. Ligand-based approaches predict interactions by deploying the similarity between the ligands of target proteins [15]. The idea is that molecules with similar structure/property would bind similar proteins [16]. But, the reliability of results might get compromised due to limited information about known ligands per protein. Docking-based approaches use the three-dimensional structure of both ...

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“…Machine learning methods in general leverage features based on the structure of drugs and targets (e.g., [9,[19][20][21]), drugs' side-effects [22], and the knowledge of already confirmed DTIs [23][24][25][26][27][28][29][30][31][32]. In particular, in case of Bipartite Local Models (BLM) [23] and its extensions (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in case of Bipartite Local Models (BLM) [23] and its extensions (e.g. [25], [32]), prediction of each DTI is based on the neighborhood of involved drug and target. Xia et al [26] proposed a semi-supervised approach based on Laplacian regularized least square method (RLS) with kernels derived from known DTIs (NetLapRLS).…”

Section: Introductionmentioning

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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Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression

Cited by 42 publications

References 51 publications

A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Drug-target interaction prediction: A Bayesian ranking approach

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