Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks-Reference-Cited by-同舟云学术

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

Published:2020-08-28 Issue:36 Volume:5 Page:22847-22851
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Tucs Andrejs¹,Tran Duy Phuoc²^ORCID,Yumoto Akiko³,Ito Yoshihiro³⁴^ORCID,Uzawa Takanori³⁴^ORCID,Tsuda Koji¹⁵⁶^ORCID

Affiliation:

1. Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan

2. School of Life Sciences and Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan

3. Emergent Bioengineering Materials Research Team, RIKEN Center for Emergent Matter Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

4. Nano Medical Engineering Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

5. RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan

6. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

Funder

New Energy and Industrial Technology Development Organization

Ministry of Education, Culture, Sports, Science and Technology

Cabinet Office, Government of Japan

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acsomega.0c02088

Reference29 articles.

1. O’Neill, J. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations; Review on Antimicrobial Resistance, 2014; Vol. 20, pp 1–16.

2. Designing antimicrobial peptides: form follows function

3. Deep learning

4. Planning chemical syntheses with deep neural networks and symbolic AI

5. Inverse molecular design using machine learning: Generative models for matter engineering

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