Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors-Reference-Cited by-同舟云学术

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Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors

Published:2016-05-10 Issue:5 Volume:19 Page:400-411
ISSN:1386-2073
Container-title:Combinatorial Chemistry & High Throughput Screening
language:en
Short-container-title:CCHTS

Author:

Kong Yue,Qu Dan,Chen Xiaoyan,Gong Ya-Nan,Yan Aixia

Publisher

Bentham Science Publishers Ltd.

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine

Cited by 12 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. Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors;ACS Omega;2024-01-02

2. Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation;Journal of Cheminformatics;2022-08-04

3. Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery;Current Topics in Medicinal Chemistry;2022-08

4. Machine learning in postgenomic biology and personalized medicine;WIREs Data Mining and Knowledge Discovery;2022-01-24

5. Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches;Journal of Chemical Information and Modeling;2021-12-21

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