Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space-Reference-Cited by-全球学者库

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

Published:2024-02-01 Issue: Volume: Page:
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Wang Mingyang¹²,Wu Zhengjian¹³,Wang Jike¹²,Weng Gaoqi¹,Kang Yu¹^ORCID,Pan Peichen¹^ORCID,Li Dan¹^ORCID,Deng Yafeng²,Yao Xiaojun⁴^ORCID,Bing Zhitong⁵,Hsieh Chang-Yu¹,Hou Tingjun¹^ORCID

Affiliation:

1. College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China

2. CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China

3. School of Computer Science, Wuhan University, Wuhan 430072, Hubei ,China

4. Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery Macau Institute for Applied Research in Medicine and Health State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China

5. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c01964

Reference87 articles.

1. Estimation of the size of drug-like chemical space based on GDB-17 data

2. De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand–Protein Interactions

3. De Novo Structure-Based Drug Design Using Deep Learning

4. De novodesign: balancing novelty and confined chemical space

5. Artificial intelligence in drug discovery: recent advances and future perspectives