Quantitative Prediction of Class I MHC/Epitope Binding Affinity Using QSAR Modeling Derived from Amino Acid Structural Information-Reference-Cited by-同舟云学术

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Quantitative Prediction of Class I MHC/Epitope Binding Affinity Using QSAR Modeling Derived from Amino Acid Structural Information

Published:2015-02-13 Issue:1 Volume:18 Page:75-82
ISSN:1386-2073
Container-title:Combinatorial Chemistry & High Throughput Screening
language:en
Short-container-title:CCHTS

Author:

Wang Yuanqiang,Zhou Pengpeng,Lin Yong,Shu Mao,Hu Yong,Xia Qingyou,Lin Zhihua

Publisher

Bentham Science Publishers Ltd.

Subject

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

Cited by 11 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. Evaluation of Antioxidant Properties and Molecular Design of Lubricant Antioxidants Based on QSPR Model;Lubricants;2023-12-22

2. HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction;Briefings in Bioinformatics;2022-05-05

3. From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling;Future Medicinal Chemistry;2019-09

4. QSAR Study on Antioxidant Tripeptides and the Antioxidant Activity of the Designed Tripeptides in Free Radical Systems;Molecules;2018-06-10

5. DeepMHC: Deep Convolutional Neural Networks for High-performance peptide-MHC Binding Affinity Prediction;2017-12-24

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