NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

By Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng. bioRxiv, 2018

332 citations recorded by the source corpus.

Motivation Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Results Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation The source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Contact zengjy321@tsinghua.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9d6b11c830329622f251782f2736a56cd58c1ab2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “The hybrid methods performed superior to other state-of-the-art methods by optimizing the feature extraction process by extracting the complex hidden features of drugs and targets [134, 144, 172, 173, 182, 197, 201, 207, 208].”
Implicit Graph Neural Networks (2020, methodology): “By iteratively aggregating information from neighboring nodes, GNN models encode graph-relational information into the representation, which then benefits a wide range of tasks, including biochemical structure discovery (Gilmer et al., 2017; Wan et al., 2019), computer vision (Kampffmeyer et al.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “Meanwhile, deep learning has clearly demonstrated its power in promoting the bioinformatics field [104, 5, 18], including sequence analysis [198, 3, 25, 156, 87, 6, 80, 169, 157, 158, 175], structure prediction and reconstruction [167, 90, 38, 168, 180, 196, 170], biomolecular property and function ”