Drug-Target Interaction Prediction Based on Interpretable Graph Transformer Model

Baozhong Zhu; Runhua Zhang; Tengsheng Jiang; Zhiming Cui; Hongjie Wu

doi:10.1007/978-981-99-4749-2_58

ScienceGate Book Chapters

BOOK-CHAPTER

Drug-Target Interaction Prediction Based on Interpretable Graph Transformer Model

Baozhong Zhu Runhua Zhang Tengsheng Jiang Zhiming Cui Hongjie Wu

Year: 2023 Lecture notes in computer science Pages: 676-686 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-99-4749-2_58

Get Full-Text PDF Get Analytical Report

Keywords:

Interpretability Computer science Classifier (UML) Artificial intelligence Graph Embedding Pattern recognition (psychology) Theoretical computer science Machine learning

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.40

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Computational Drug Discovery Methods

Physical Sciences → Computer Science → Computational Theory and Mathematics

Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Bioinformatics and Genomic Networks

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Drug-Target Interaction Prediction Based on Interpretable Graph Transformer Model

Metrics

Topics

Related Documents

TGTNet: Graph Transformer-Based Architecture for Drug-Target Interaction Prediction

MotifGT-DTI: Pivotal Motif-Based Graph Transformer Model Improves Drug–Target Interaction Prediction

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction

Improved drug–target interaction prediction with intermolecular graph transformer

Drug-Target Interaction Prediction Based on Transformer