The popularity of ChatGPT is no less than that of AlphaGo, which was born in 2016, and AlphaFold2 in 2020: the former has driven the implementation of computer vision in medical care, and the latter has approached the technical limit in specific fields of life sciences. Today, can the generative AI technology represented by ChatGPT become the technical engine for further breakthroughs in life sciences?
Recently, ChatGPT has caused quite a stir in various industries.
Microsoft “increased its position”, the US version of the headline BuzzFeed “bet”, and even the domestic technology giants, Baidu, Ali, Tencent, NetEase, JD.com, 360, Byte… These familiar Chinese Internet companies have also entered and exited one after another. information.
The AI circle, which has been silent for a long time, seems to have returned to the enthusiasm of the AlphaGo era 7 years ago.
ChatGPT is not only regarded as a simple new generation of chatbots, it even represents a revolutionary turning point in artificial intelligence.
Bill Gates, who had previously given the ChatGPT platform, also said that artificial intelligence such as ChatGPT can provide optimization for reading and writing, and actually improve work efficiency in fields such as healthcare and education. “Reading and writing are now within the capabilities of artificial intelligence.” Inside, this will have very wide-ranging implications.”
The fire of ChatGPT also quickly spread to the AI life science circle.
Recently, Profluent, a start-up company in Berkeley, California, reported that it used Progen, a protein engineering deep learning language model similar to ChatGPT, to achieve AI prediction of protein synthesis for the first time.
In this regard, there are different opinions in the industry, and the optimists say,“This new technique could ‘beyond the Nobel Prize’ – more powerful than directed evolution of protein design in 2018.”
In fact, as early as the end of 2022, three foreign companies are competing to release a new model of AIGC × protein design.
The David Baker team at the University of Washington released RFdiffusion, which uses diffusion models to design a variety of new single-chain proteins and complexes that are completely different from natural proteins;
Generate Biomedicines released Chroma, a diffusion protein generation model, which can design protein sequences and structures according to preset requirements;
Meta’s ESM2 uses a protein sequence pre-training large model based on 15 billion parameters to achieve programmable protein de novo design.
Not long ago, the domestic AI company Tianlang also announced its progress and released an upgraded version of the protein design model – TRDiffusion. The results show that this model is not only suitable for the supported “one-click generation of proteins that meet the description”, but also increases the number of protein designs by more than 2 times under the same computing resource configuration and time.
Xue Guirong, the founder of Skyland, told Leifeng.com & “Medical and Health AI Nuggets”, “Although the technical paths of these companies are different, the concentrated explosion of these models and algorithms means that we have entered a new era of programmable biology. In other words, industries such as drug development, vaccine development, biosynthesis, and clean energy related to protein design are about to usher in a key point of industrial explosion.”
An industry insider even said bluntly, “ChatGPT is as hot today as AlphaGo, which was born in 2016, and AlphaFold2 in 2020: the former has driven the implementation of computer vision in medical care, and the latter has given birth to discussions in life sciences. Popularity. Today, the generative AI technology represented by ChatGPT may become a ‘catalyst’ for the implementation of life science technology.”
However, judging from the practical experience of the past few years, AI, which is characterized by computer vision, has not had a “revolutionary” subversive effect in the medical field.
Can AIGC represented by ChatGPT be comparable to the emergence of AlphaGo or AlphaFold2?
What achievements in AI life science can be called AIGC? Which ones might just be hype?
What will be the most obvious change in the field of life sciences when AIGC flexes its muscles? For life sciences, is there any uncertainty or crisis in AIGC?
The emergence of these problems has an unusual space for discussion for practitioners in the field of life sciences and computers.
To this end, Leifeng.com will hold a“Can the fire of ChatGPT burn to the AI life science community?” “online roundtable forum.
The forum will be chaired by Gong Xinqi, School of Mathematics, Renmin University of China, Pan Yi, Dean of the School of Computer Science and Control Engineering, Shenzhen University of Technology Zhou Yaoqi, a senior researcher at the Institute of Physical Biology, Xu Jinbo, the founder of Molecular Heart, and Xue Guirong, the founder of Skyland, participated in the discussion.
Readers can scan the QR code on the poster at the bottom of the article to enter the expert community. We will collect and summarize your questions, feed them back to the participants, and answer them in the discussion session.
Guest profile
Moderator: Gong Xinqi, Professor of School of Mathematics, Renmin University of China
Professor of the Academy of Mathematical Sciences, Renmin University of China, head of the Mathematical Intelligence Application Laboratory, director of the Chinese Bioinformatics Society (preparation), director of the Computational Systems Biology Branch of the Operational Research Society, and a senior member of the China Computer Federation. Co-researcher of Beijing Advanced Center for Structural Biology of Tsinghua University, Distinguished Researcher of Beijing Zhiyuan Artificial Intelligence Research Institute, Visiting Scholar of Harvard University.
Professor Gong Xinqi’s main research interest is the calculation of protein interaction complexes. He has published more than 70 SCI academic papers, including theoretical models, calculation methods and biological applications. The published journals include Nature, Science, Nature Communications, Proteins and Science In China and other academic journals, the papers have been cited more than 2900 times, and the H index is 26.
Yi Pan, Shenzhen University of Technology, Chinese Academy of Sciences (in preparation), School of Computer Science and Control Engineering, Dean
Dean of the School of Computer Science and Control Engineering, Shenzhen University of Technology, Chinese Academy of Sciences, chair professor, academician of the American Academy of Medicine and Biological Engineering, foreign academician of the Ukrainian National Academy of Engineering, academician of the European Academy of Sciences and Arts, and chairman of the Metaverse Working Committee of the China Democratic Association.
Academician Pan Yi received his bachelor’s and master’s degrees from the Department of Computer Science and Engineering of Tsinghua University. He studied in the United States in 1987 and received his Ph.D. in computer science from the University of Pittsburgh in 1991. Since then, he has mainly worked at Georgia State University in the United States. In the past ten years, Professor Pan Yi has successively served as the head of the Department of Computer Science and Department of Biology, and the deputy dean of the College of Arts and Sciences.
Academician Pan Yi’s main research field is to use cloud computing, big data analysis, artificial intelligence, deep learning, etc. as tools to conduct research on bioinformatics and medical information. He has published more than 250 academic papers in SCI journals in this field, of which more than 100 were published in top IEEE/ACM Transactions/Journals academic journals; he also published more than 150 academic papers in international academic conference proceedings, and published and edited more than 40 book. His academic achievements have been cited more than 20,000, and the current H-index is 90.
Song Le, Chief AI Scientist of Baitu Biotech
A world-renowned machine learning expert, now the chief technology officer and chief AI scientist of Baitu Biotech, responsible for the research and development of wet and dry closed-loop high-throughput biological computing engines and related strategic work.
Dr. Song Le is a well-known expert in machine learning and graph deep learning. He used to be a tenured professor of the Georgia Institute of Technology School of Computer Science, deputy director of the Machine Learning Center, director of the Department of Machine Learning at MBZUAI in the United Arab Emirates, and the leader of the deep learning team of Ant Financial (P10). A researcher at Alibaba DAMO Academy, a member of the board of directors of the International Machine Learning Conference, has rich experience in AI algorithms and engineering.
Since 2008, Dr. Song Le has been engaged in biocomputing-related research at Carnegie Mellon University. He has made a series of breakthroughs in target mining and drug design using machine learning technology, and has obtained major machine learning technologies such as NeurIPS, ICML, and AISTATS. Conference Best Paper Award. In terms of community service, he has served as the field chairman of AI top conferences such as NeurIPS, ICML, ICLR, AAAI, and IJCAI, and the general chairman of ICML 2022. He is also the associate editor of peer-reviewed journals JMLR and IEEE TPAMI.
Yaoqi Zhou, Senior Researcher, Institute of Systems and Physical Biology, Shenzhen Bay Laboratory
Senior researcher at the Institute of Systems and Physical Biology, Shenzhen Bay Laboratory. At present, Zhou Yaoqi’s research group is engaged in basic research on the relationship between protein/RNA sequence, structure and function, and applied research on biomolecular detection and drug development through the combination of calculation and experiment.
Professor Zhou Yaoqi has joined the Shenzhen Bay Laboratory full-time since March 2021. He received his bachelor’s degree in modern chemistry from the University of Science and Technology of China in 1984, his Ph.D. in chemical physics from Stony Brook University in New York in 1990, and his Ph. , a postdoctoral fellow at Harvard University, served as an assistant professor at the State University of New York at Buffalo in 2000, was promoted to a tenured associate professor in 2004, and became a tenured full professor at the School of Information and Medicine at Indiana University in 2006. From 2013 to 2021, he served as a tenured professor at Griffith University in Australia Full Professor at the Institute of Glycoomics.
Professor Zhou Yaoqi has been working in structural bioinformatics for a long time, and has been among the best in international protein structure prediction and function prediction competitions many times. So far, he has published more than 250 papers, cited more than 16,000 times, and has an H-factor of 68.
Jinbo Xu, founder of Molecular Heart, tenured professor at Toyota Institute of Computing Technology in Chicago, USA
The founder of Molecular Heart, a professor at the Toyota Institute of Computing Technology in Chicago, the editorial board member of “Bioinformatics”, and a visiting scholar at the Intelligent Industry Research Institute (AIR) of Tsinghua University, focusing on research topics such as artificial intelligence and computational biology.
Professor Xu Jinbo graduated from the Computer Department of the University of Science and Technology of China with a bachelor’s degree and a master’s degree from the Institute of Computing Technology, Chinese Academy of Sciences. In 2003, he received a doctorate from the University of Waterloo, Canada, and then entered the Department of Mathematics and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology for postdoctoral work. Returned to China and founded the company Molecule Heart.
Professor Xu Jinbo’s research interests include artificial intelligence and machine learning, optimization algorithms, and computational biology. In 2016, he proved for the first time that deep learning can greatly improve the performance of protein structure prediction, leading the breakthrough of artificial intelligence in the field of protein structure prediction.
Professor Xu Jinbo has won the US Sloan Research Award, the US Natural Science Foundation Early Career Award, the “PLoS Computational Biology” Innovation Breakthrough Award, the top international computational biology conference RECOMB Best Paper Award and the Test-of-Time Award (Test-of-Time Award) .
Xue Guirong, founder and CEO of Skyland
Founder and CEO of Skyland, a scientist in the field of artificial intelligence and big data, a member of the cloud computing expert group of the Ministry of Science and Technology, a distinguished professor of big data major of Beihang University, KDD China branch, CCF artificial intelligence committee, CCF big data committee Committee member, chairman of ACM WWW Internet Monetization field.
Dr. Xue Guirong received a Ph.D. in Computer Science from Shanghai Jiaotong University in 2006, and was one of the first batch of winners of the National Excellent Doctoral Dissertation Award. From 2006 to 2009, he served as an associate professor and special researcher in the Computer Department of Shanghai Jiao Tong University. Joined Ali in 2009 as the head of Alimama Big Data Center and chief data scientist. Founded Tianrang in 2016, and led the team to make important breakthroughs in AI Go, transportation, finance, biology and other fields.
Dr. Xue Guirong’s research direction is mainly machine learning, information retrieval, Internet marketing, large-scale data analysis and distributed computing. NIPS, ICML, SIGKDD, SIGIR, WWW and the world‘s top journals ACM TOIS, ACM TIST, etc. have published more than 70 papers, have more than ten patents, and have more than 9000 paper citations.
Lecture information
Topic: Can the fire of ChatGPT spread to the AI life science community?
Time: February 15th, Beijing time 20:00-22:00
Sponsor: Leifeng.com GAIR Live & “Medical and Health AI Nuggets”
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