Advanced Deep Learning Methods for Protein Structure Prediction and Design
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917971129008128 |
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| author | Zhang, Yichao Deng, Ningyuan Song, Xinyuan Bi, Ziqian Wang, Tianyang Yao, Zheyu Chen, Keyu Li, Ming Niu, Qian Liu, Junyu Peng, Benji Zhang, Sen Liu, Ming Zhang, Li Pan, Xuanhe Wang, Jinlang Feng, Pohsun Wen, Yizhu Yan, Lawrence KQ Tseng, Hongming Zhong, Yan Wang, Yunze Qin, Ziyuan Jing, Bowen Yang, Junjie Zhou, Jun Liang, Chia Xin Song, Junhao |
| author_facet | Zhang, Yichao Deng, Ningyuan Song, Xinyuan Bi, Ziqian Wang, Tianyang Yao, Zheyu Chen, Keyu Li, Ming Niu, Qian Liu, Junyu Peng, Benji Zhang, Sen Liu, Ming Zhang, Li Pan, Xuanhe Wang, Jinlang Feng, Pohsun Wen, Yizhu Yan, Lawrence KQ Tseng, Hongming Zhong, Yan Wang, Yunze Qin, Ziyuan Jing, Bowen Yang, Junjie Zhou, Jun Liang, Chia Xin Song, Junhao |
| contents | After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Deep Learning Methods for Protein Structure Prediction and Design Zhang, Yichao Deng, Ningyuan Song, Xinyuan Bi, Ziqian Wang, Tianyang Yao, Zheyu Chen, Keyu Li, Ming Niu, Qian Liu, Junyu Peng, Benji Zhang, Sen Liu, Ming Zhang, Li Pan, Xuanhe Wang, Jinlang Feng, Pohsun Wen, Yizhu Yan, Lawrence KQ Tseng, Hongming Zhong, Yan Wang, Yunze Qin, Ziyuan Jing, Bowen Yang, Junjie Zhou, Jun Liang, Chia Xin Song, Junhao Biomolecules Artificial Intelligence Machine Learning After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students. |
| title | Advanced Deep Learning Methods for Protein Structure Prediction and Design |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.13522 |