Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Pharmaceutics
Hanbi Lee, Wankyu Kim

Abstract

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein-protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction u...Continue Reading

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Citations

May 23, 2020·International Journal of Molecular Sciences·Neann Mathai, Johannes Kirchmair
Oct 14, 2020·Briefings in Bioinformatics·Maxat KulmanovRobert Hoehndorf

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Methods Mentioned

BETA
gene knockdown
feature extraction

Software Mentioned

CMAP
TensorFlow
Connectivity Map CMAP
Keras
GloVe
node2vec
learn
scikit

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