Drug repositioning: a machine-learning approach through data integration

By F. Napolitano, Yan Zhao, V. Moreira, Roberto Tagliaferri, J. Kere, M. D’Amato, Dario Greco. 2013

313 citations recorded by the source corpus.

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Review of Drug Repositioning Approaches and Resources (2018, background): “Most existing computational approaches are based on the gene expression response of cell lines after treatment or merging several types of information about disease-drug relationships [18] that can be divided into different types from different viewpoints [19-21] .”
A review of connectivity map and computational approaches in pharmacogenomics (2017, background): “However, for either type of investigations, machine learning [6] and biomedical text mining [7] approaches have been vital to uncover hidden relationships between drugs and potential new indications.”
A survey of current trends in computational drug repositioning (2016, background): “[59] focused on a drug-centered approach to predict drug Computational Drug Repositioning | 5”
Machine learning applications in drug development (2019, methodology): “Integrating multi-view data in a drug development method has been shown to increase its accuracy [94–96].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “Recently, in order to deal with high dimensional and oftentimes noisy data in DTI predictions in general and in drug repurposing in particular, authors in [115–117] proposed and developed deep learning algorithms in the DTI’s machine learning approaches.”