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Zhong Michal Zielinski Augustin \u017d\u00eddek Victor Bapst Pushmeet Kohli Max Jaderberg Demis Hassabis and John\u00a0M. Jumper. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630 8016 (2024) 493\u2013500.","DOI":"10.1038\/s41586-024-07487-w"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Gustaf Ahdritz Nazim Bouatta Christina Floristean Sachin Kadyan Qinghui Xia William Gerecke Timothy\u00a0J. O\u2019Donnell Daniel Berenberg Ian Fisk Niccol\u00f2 Zanichelli Bo Zhang Arkadiusz Nowaczynski Bei Wang Marta\u00a0M. Stepniewska-Dziubinska Shang Zhang Adegoke Ojewole Murat\u00a0Efe Guney Stella Biderman Andrew\u00a0M. Watkins Stephen Ra Pablo\u00a0Ribalta Lorenzo Lucas Nivon Brian Weitzner Yih-En\u00a0Andrew Ban Shiyang Chen Minjia Zhang Conglong Li Shuaiwen\u00a0Leon Song Yuxiong He Peter\u00a0K. Sorger Emad Mostaque Zhao Zhang Richard Bonneau and Mohammed AlQuraishi. 2024. 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Design Compiler. https:\/\/www.synopsys.com\/implemet ation-and-signoff\/rtl-synthesis-test\/design-compiler-graphical.html."},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN60899.2024.10651470"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"John Jumper Richard Evans Alexander Pritzel Tim Green Michael Figurnov Olaf Ronneberger Kathryn Tunyasuvunakool Russ Bates Augustin \u017d\u00eddek Anna Potapenko Alex Bridgland Clemens Meyer Simon A.\u00a0A. Kohl Andrew\u00a0J. Ballard Andrew Cowie Bernardino Romera-Paredes Stanislav Nikolov Rishub Jain Jonas Adler Trevor Back Stig Petersen David Reiman Ellen Clancy Michal Zielinski Martin Steinegger Michalina Pacholska Tamas Berghammer Sebastian Bodenstein David Silver Oriol Vinyals Andrew\u00a0W. Senior Koray Kavukcuoglu Pushmeet Kohli and Demis Hassabis. 2021. 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HBM2E memory. https:\/\/www.micron.com\/products\/memory\/ hbm\/hbm2e."},{"key":"e_1_3_3_1_43_2","unstructured":"Shervin Minaee Tomas Mikolov Narjes Nikzad Meysam Chenaghlu Richard Socher Xavier Amatriain and Jianfeng Gao. 2024. Large language models: A survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.06196 (2024)."},{"key":"e_1_3_3_1_44_2","doi-asserted-by":"crossref","unstructured":"Milot Mirdita Konstantin Schutze Yoshitaka Moriwaki Lim Heo Sergey Ovchinnikov and Martin Steinegger. 2022. Easy and accurate protein structure prediction using ColabFold. Nature Methods 19 (2022) 679\u2013682.","DOI":"10.1038\/s41592-022-01488-1"},{"key":"e_1_3_3_1_45_2","unstructured":"Markus Nagel Marios Fournarakis Rana\u00a0Ali Amjad Yelysei Bondarenko Mart Van\u00a0Baalen and Tijmen Blankevoort. 2021. A white paper on neural network quantization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2106.08295 (2021)."},{"key":"e_1_3_3_1_46_2","unstructured":"NVIDIA. 2024. 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The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain. https:\/\/www.pnas.org\/doi\/abs\/10.1073\/pnas.37.4.205. Proceedings of the National Academy of Sciences 37 4 (1951) 205\u2013211.","DOI":"10.1073\/pnas.37.4.205"},{"key":"e_1_3_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Shuang Peng Fei Yang Ning Sun Sheng Chen Yanfeng Jiang and Aimin Pan. 2023. Exploring Post-Training Quantization of Protein Language Models. https:\/\/arxiv.org\/abs\/2310.19624.","DOI":"10.1109\/BIBM58861.2023.10385775"},{"key":"e_1_3_3_1_53_2","unstructured":"Markus\u00a0N Rabe and Charles Staats. 2021. Self-attention does not need O(n2) memory. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2112.05682 (2021)."},{"key":"e_1_3_3_1_54_2","doi-asserted-by":"crossref","unstructured":"Andrew\u00a0W. Senior Richard Evans John Jumper James Kirkpatrick Laurent Sifre Tim Green Chongli Qin Augustin \u017d\u00eddek Alexander W.\u00a0R. 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