Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	60114
Country	Japan

Received	2020-10-15 12:11
Peer-Review Started	2020-10-15 12:11
To Make the First Decision
Return for Revision	2020-12-17 19:33
Revised	2020-12-29 06:58
Second Decision	2021-02-03 03:36
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-02-12 18:46
Articles in Press	2021-02-12 18:46
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-02-19 09:35
Publish the Manuscript Online	2021-02-25 04:24

ISSN	2644-3236 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites
Manuscript Source	Invited Manuscript
All Author List	Tatsuo Kanda, Reina Sasaki, Ryota Masuzaki and Mitsuhiko Moriyama
Funding Agency and Grant Number	Funding Agency Grant Number The Japan Agency for Medical Research and Development JP20fk0210075
Corresponding Author	Tatsuo Kanda, MD, PhD, Associate Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku 173-8610, Tokyo, Japan. kanda.tatsuo@nihon-u.ac.jp
Key Words	Artificial intelligence; Hepatitis A virus internal ribosomal entry sites; Cap-independent translation; Antivirals; Severe hepatitis A; Glucose-regulated protein 78
Core Tip	In certain areas, it is difficult to perform universal hepatitis A virus (HAV) vaccination. We found that several drugs potentially inhibit HAV internal ribosomal entry sites-dependent translation and HAV replication. After the application of machine and deep learning, artificial intelligence identified effective anti-HAV drugs more quickly, using drug repositioning and drug rescue.
Publish Date	2021-02-25 04:24
Citation	Kanda T, Sasaki R, Masuzaki R, Moriyama M. Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites. Artif Intell Gastroenterol 2021; 2(1): 1-9
URL	https://www.wjgnet.com/2644-3236/full/v2/i1/1.htm
DOI	https://dx.doi.org/10.35712/aig.v2.i1.1

Full Article (PDF)	AIG-2-1.pdf
Full Article (Word)	AIG-2-1.docx
Manuscript File	60114_Auto_Edited_LM.docx
Answering Reviewers	60114-Answering reviewers.pdf
Audio Core Tip	60114-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	60114-Conflict-of-interest statement.pdf
Copyright License Agreement	60114-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	60114-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate	60114-Language certificate.pdf
Peer-review Report	60114-Peer-review(s).pdf
Scientific Misconduct Check	60114-Scientific misconduct check.pdf
Scientific Editor Work List	60114-Scientific editor work list.pdf