Molecule Generation for Drug Design: A Graph Learning Perspective

Authors

Nian Zu Yang, Shanghai Jiao Tong University, China
Huaijin Wu, Shanghai Jiao Tong University, China
Junchi Yan, Shanghai Jiao Tong University, China
Xiaoyong Pan, Shanghai Jiao Tong University, China
Ye Yuan, Shanghai Jiao Tong University, China
Le Song, BioMap & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

arXiv

Abstract

Machine learning has revolutionized many fields, and graph learning is recently receiving increasing attention. From the application perspective, one of the emerging and attractive areas is aiding the design and discovery of molecules, especially in drug industry. In this survey, we provide an overview of the state-of-the-art molecule (and mostly for de novo drug) design and discovery aiding methods whose methodology involves (deep) graph learning. Specifically, we propose to categorize these methods into three groups: i) all at once, ii) fragment-based and iii) node-by-node. We further present some representative public datasets and summarize commonly utilized evaluation metrics for generation and optimization, respectively. Finally, we discuss challenges and directions for future research, from the drug design perspective. Copyright © 2022, The Authors. All rights reserved.

DOI

10.48550/arXiv.2202.09212

Publication Date

2-18-2022

Comments

Preprint: arXiv

Recommended Citation

N. Yang, H. Wu, J. Yan, X. Pan, Y. Yuan, and L. Song, "Molecule generation for drug design: a graph learning perspective," 2022, arXiv:2202.09212