마틴 스타이네거 교수팀과 하버드대학 및 막스 플랑크 연구소의 공동 연구,  단백질 구조예측 플랫폼 ColabFold, 2022년 인공지능분야 최다 인용 논문 2위로 선정 (Paper from Prof. Martin Steinegger's collaboration with Harvard and Max Planck is ranked as the second most cited AI paper of 2022.)

<Abstract>
ColabFold is a fast, accurate and user-friendly protein folding method based on the ground-breaking AlphaFold2 model. It was ranked as the second most cited AI paper in 2022 by Zeta Alpha.



The development of ColabFold marks a significant achievement in the field of protein structure prediction. The paper published in Nature Methods in 2022 describes a new platform for protein folding that combines the speed and accuracy of MMseqs2 generated multiple sequence alignments with the structure prediction capabilities of AlphaFold2. ColabFold is as accurate as AlphaFold2 while being up to 100x faster. ColabFold is a free and accessible platform that can be utilized within Google Colab or, alternatively, with a local installation, where a user can predict close to 1,000 protein structures per day on a single graphics processing unit.

Since its release, ColabFold has become widely used in the scientific community, with over 5 million jobs processed and over 1200 citations, making it the second most cited AI paper in 2022 according to Zeta Alpha. The speed and accuracy of ColabFold makes it an invaluable tool in many different areas of research, from drug discovery to understanding the structures of proteins involved in diseases.

This work was a collaboration between Prof. Steinegger's Lab from SNU with Dr. Ovchinnikov from Harvard and Dr. Mirdita from Max Planck with further contributions of Mr. Schütze (TU Munich), Dr. Moriwaki (U. Tokyo) and Dr. Lim (Michigan State University). Prof. Steinegger has previously collaborated with DeepMind on AlphaFold2 and is a co-author on the manuscript, the most cited AI article in 2021. Hardware resources for ColabFold were kindly provided by the Korean Bioinformatics Center (KOBIC).



https://twitter.com/ZetaVector/status/1631590029926494211 
https://www.zeta-alpha.com/post/must-read-the-100-most-cited-ai-papers-in-2022 
논문: https://www.nature.com/articles/s41592-022-01488-1