Application of Protein Language Models to low-N Engineering of metalloproteinase inhibitors-Reference-Cited by-同舟云学术

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Application of Protein Language Models to low-N Engineering of metalloproteinase inhibitors

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Computational Science and Computational Intelligence (CSCI)
language:
Short-container-title:

Author:

Buxton Elham Khorasani¹,Patel Rushabh²,Toumaian Mari R.³,Raeeszadeh-Sarmazdeh Maryam³

Affiliation:

1. University of Illinois at Springfield,Department of Computer Science,Springfield,IL

2. Temple University,Department of Computer and Information Sciences,Philadelphia,PA

3. University of Nevada,Chemical and Materials Engineering Department,Reno,NV

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9798893/9798894/09799140.pdf?arnumber=9799140

Reference32 articles.

1. Machine-learning-guided directed evolution for protein engineering

2. Advances in machine learning for directed evolution

3. Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

4. Modeling aspects of the language of life through transfer-learning protein sequences

5. NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning

Cited by 1 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. An extended de Bruijn graph for feature engineering over biological sequential data;Machine Learning: Science and Technology;2024-07-19

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