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Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information

Yifan Shang¹, Xiucai Ye¹, Yasunori Futamura¹

¹Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.

Briefings in Bioinformatics

|March 9, 2022

Summary

This study introduces MccDTI, a new computational framework for predicting drug-target interactions (DTIs). MccDTI effectively integrates diverse data, improving DTI prediction accuracy and accelerating drug discovery.

Keywords:

deep learning drug-target interactions prediction multiview network embedding

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Area of Science:

Computational biology
Bioinformatics
Drug discovery

Background:

Accurate drug-target interaction (DTI) prediction is crucial for efficient drug discovery and repositioning.
Existing methods often fail to capture complex relationships across diverse data sources.

Purpose of the Study:

To develop a novel computational framework, MccDTI, for enhanced DTI prediction.
To integrate heterogeneous drug and target information using multiview network embedding.

Main Methods:

MccDTI employs multiview network embedding to learn low-dimensional representations of drugs and targets.
It preserves consistent and complementary information across different data views.
A matrix completion scheme is utilized for DTI prediction based on learned representations.

Main Results:

MccDTI demonstrated superior prediction accuracy compared to four state-of-the-art methods on two benchmark datasets.
Literature verification confirmed the reliability of potential DTIs predicted by MccDTI.

Conclusions:

MccDTI offers a powerful computational tool for predicting novel drug-target interactions.
The framework has the potential to significantly accelerate the drug discovery pipeline.