Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

By Yoshihiro Yamanishi, Masaaki Kotera, Minoru Kanehisa, Susumu Goto. Bioinform., 2010

457 citations recorded by the source corpus.

Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. 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. Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/. Availability: Softwares are available upon request. Contact: yoshihiro.yamanishi@ensmp.fr

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/08e40b6e41374f57fbc0006f816608acefbcc10a

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Citations in the corpus

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, methodology): “To overcome the shortcomings of these two approaches, several statistical models have been developed to predict the interactions, such as the binary classification models [36,37] and the supervised bipartite graph inference ones [38,39].”
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012, methodology): “Excellent performance was obtained for these methods on four benchmark data sets, which outperformed some methods reported elsewhere [17,28,29].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Complex chemical similarity networks (Section 3.1.3) also use a combination of network-related data including e.g. interactomes for the prediction of off-target effects (Hase et al., 2009; Yamanishi et al., 2010; Zhao & Li, 2010).”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “(iii) Other approaches : We can use other information, including pharmacological information of drugs [33] and biomedical documents, from which implicit co-occurrent compound–protein relations can be extracted by text mining techniques [34].”
Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data (2011, unknown): “their targets and pharmacology [16,17].”
Interpretable Drug Target Prediction Using Deep Neural Representation (2018, methodology): “, 2007]; (2) an aggregation of expert designed similarity measures to derive candidate pairs from known DTI pairs [Ding et al., 2013; Yamanishi et al., 2010]; and (3) representations learned from descriptors from (1), for example, using techniques such as Restricted Boltzmann machine or Autoencoder ”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “…DTI using various types of biological data such as chemical structures, protein domain and drug side effects (note that studies that primarily focused on side effect are excluded in this paper [ 59–62]) and three supervised algorithms (BGL [13, 143], BLM [101] and pairwise kernel regression [9]).”
Deep learning with feature embedding for compound-protein interaction prediction (2016, unknown): “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].”