Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

By Yoshihiro Yamanishi, M. Araki, Alex Gutteridge, Wataru Honda, Minoru Kanehisa. Intelligent Systems in Molecular Biology, 2008

1,081 citations recorded by the source corpus.

Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cb239560296d0dc0bea4ccbcc6a4eefa79b8f100

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Citations in the corpus

Human Disease-Drug Network Based on Genomic Expression Profiles (2009, methodology): “For the drug-drug relationship and target deconvolution, we can integrate the results derived from assessing drug side-effect and chemical similarity, target sequence similarity and drug-target network [4,12,60].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “Additionally, this process cannot be applied if the 3D structure of the protein is unknown [ 13].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “, which utilized a kernel regression method to use the information on known drug interactions as the input to identify new DTIs, combining a chemical space and genomic spaces into a pharmacological space [3].”
Predicting drug-target interactions using restricted Boltzmann machines (2013, methodology): “In Yamanishi et al. (2008), a supervised learning framework was developed based on a bipartite graph, which integrates both chemical and genomic spaces by mapping them into a unified space.”
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012, methodology): “developed a bipartite graph learning method to predict DTI by integrating chemical and genomic spaces [17].”
Deep learning with feature embedding for compound-protein interaction prediction (2016, positive): “Numerous computational methods have been proposed to predict potential compound-protein interactions in silico to narrow the large search space of possible interacting compound-protein pairs and facilitate the drug discovery and development process [7] [8] [9] [10] [11] [12] [13] [14].”
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, methodology): “We used four real benchmark datasets, called Nuclear receptor , GPCR , Ion channel and Enzyme , which were originally provided by [29] 1 .”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “The paper 70 introduced a bipartite graph-learning method based on kernel regression to learn a co-mapping of drugs in chemical space and targets (proteins) in genomic space into a common pharmacological space.”
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, neutral): “families) of targets that are enzymes, ion channels, Gprotein coupled receptors (GPCRs) and nuclear receptors [90].”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, neutral): “where h d is the ith column of H((3)) d and h j p is the jth column of H (3) p , respectively, denoting the ith and jth sub-structural representations of drug and protein.”
Gaussian interaction profile kernels for predicting drug-target interaction (2011, methodology): “The number of drugs and target proteins, their ratio, and the number of interactions in the drug–target datasets from Yamanishi et al. (2008).”
Toward more realistic drug–target interaction predictions (2014, positive): “For the target sequence similarities, we tried out both the original and normalized versions of the Smith–Waterman (SW) score [8, 13].”