Application advances of deep learning methods for de novo drug design and molecular dynamics simulation-Reference-Cited by-同舟云学术

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Published:2021-10-14 Issue: Volume: Page:
ISSN:1759-0876
Container-title:WIREs Computational Molecular Science
language:en
Short-container-title:WIREs Comput Mol Sci

Author:

Bai Qifeng¹^ORCID,Liu Shuo²,Tian Yanan²,Xu Tingyang³,Banegas‐Luna Antonio Jesús⁴,Pérez‐Sánchez Horacio⁴^ORCID,Huang Junzhou³,Liu Huanxiang²,Yao Xiaojun⁵

Affiliation:

1. Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China

2. School of Pharmacy Lanzhou University Lanzhou Gansu China

3. Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China

4. Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain

5. College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China

Funder

Lanzhou University

Publisher

Wiley

Subject

Materials Chemistry,Computational Mathematics,Physical and Theoretical Chemistry,Computer Science Applications,Biochemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1581

Reference153 articles.

1. Evolutionary algorithms for de novo drug design—a survey;Devi RV;Appl Soft Comput,2015

2. Efficient drug lead discovery and optimization;Jorgensen WL;Acc Chem Res,2009