DeepCompoundNet: enhancing compound–protein interaction prediction with multimodal convolutional neural networks-Reference-Cited by-同舟云学术

DeepCompoundNet: enhancing compound–protein interaction prediction with multimodal convolutional neural networks

Published:2023-12-12 Issue: Volume: Page:1-10
ISSN:0739-1102
Container-title:Journal of Biomolecular Structure and Dynamics
language:en
Short-container-title:Journal of Biomolecular Structure and Dynamics

Author:

Palhamkhani Farnaz¹,Alipour Milad²,Dehnad Abbas³,Abbasi Karim⁴,Razzaghi Parvin⁵,Ghasemi Jahan B.¹

Affiliation:

1. Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran, Iran

2. Department of Interdisciplinary Technologies, Network Science and Technology, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran

3. Faculty of Mathematics and Computer Science, Allameh Tabatabai University, Tehran, Iran

4. Laboratory of System Biology, Bioinformatics & Artificial Intelligence in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran

5. Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

Publisher

Informa UK Limited

Subject

Molecular Biology,General Medicine,Structural Biology

Link

https://www.tandfonline.com/doi/pdf/10.1080/07391102.2023.2291829

Reference54 articles.

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3. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation;Bai Q.;Wiley Interdisciplinary Reviews: Computational Molecular Science,,2022

4. Systematic comparison of the protein-protein interaction network of bacterial Universal stress protein A (UspA): an insight into its discrete functions

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