PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions-Reference-Cited by-同舟云学术

PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions

Published:2022 Issue:13 Volume:13 Page:3661-3673
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Moon Seokhyun¹^ORCID,Zhung Wonho¹^ORCID,Yang Soojung¹^ORCID,Lim Jaechang²^ORCID,Kim Woo Youn¹²³^ORCID

Affiliation:

1. Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

2. HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea

3. KI for Artificial Intelligence, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

Abstract

PIGNet, a deep neural network-based drug–target interaction model guided by physics and extensive data augmentation, shows significantly improved generalization ability and model performance.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D1SC06946B

Reference83 articles.

1. Applications of Deep Learning in Biomedicine

2. Deep Learning and Its Applications in Biomedicine

3. Deep Learning in the Biomedical Applications: Recent and Future Status

4. Deep learning in biomedicine

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