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De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.<\/jats:p>","DOI":"10.1093\/bib\/bbac102","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T20:09:31Z","timestamp":1646165371000},"source":"Crossref","is-referenced-by-count":68,"title":["Protein design via deep learning"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-0940-2885","authenticated-orcid":false,"given":"Wenze","family":"Ding","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China"},{"name":"Beijing Advanced Innovation Center for 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