Mining Toxicity Information from Large Amounts of Toxicity Data-Reference-Cited by-同舟云学术

Mining Toxicity Information from Large Amounts of Toxicity Data

Published:2021-05-07 Issue:10 Volume:64 Page:6924-6936
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Wu Zhenxing¹²^ORCID,Jiang Dejun¹^ORCID,Wang Jike¹³,Hsieh Chang-Yu⁴,Cao Dongsheng⁵^ORCID,Hou Tingjun¹²^ORCID

Affiliation:

1. Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China

2. State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China

3. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072 Hubei, P. R. China

4. Tencent Quantum Laboratory, Tencent, Shenzhen 518057 Guangdong, P. R. China

5. Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004 Hunan, P. R. China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.1c00421

Reference60 articles.

1. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates

2. A low-cost, high-quality new drug discovery process using patient-derived induced pluripotent stem cells

3. From 3D cell culture to organs-on-chips

4. Reconstituting Organ-Level Lung Functions on a Chip

5. Corrigendum: In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

Cited by 53 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models;Science of The Total Environment;2024-11

2. MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs with Pixel Masking;2024-09-09

3. Multi-task pretrained language model with novel application domains enables more comprehensive health and ecological toxicity prediction;Journal of Hazardous Materials;2024-09

4. MultiCBlo: Enhancing predictions of compound-induced inhibition of cardiac ion channels with advanced multimodal learning;International Journal of Biological Macromolecules;2024-09

5. Deep active learning with high structural discriminability for molecular mutagenicity prediction;Communications Biology;2024-08-31