How does Life science move towards AI4S era?

In the field of genomics, through machine learning and statistical models, researchers have successfully predicted important biological processes such as the function of encoded proteins in the genome, gene shear events, and regulatory networks, providing a huge data resource for biological science research.

Under the new paradigm of AI4S, the pre-trained model shows unprecedented capabilities, but for specific scientific data and information, it also needs to deeply combine the underlying characteristics of the discipline, and use its special "language" as a carrier, such as protein sequence and nucleic acid sequence in life science, which is different from natural language to carry information.

In RNA drug design, researchers urgently need a computational tool that can efficiently and comprehensively explore and describe the RNA space in order to achieve digital innovation in RNA research. Uni-RNA developed by Deep potential technology came into being. According to Wen Han, head of macromolecular research and development of Deep Potential Technology, Uni-RNA uses about 1 billion high-quality RNA sequences for large-scale pre-training, covering almost all RNA space, and fully mining the potential information of RNA sequences. By fine-tuning the model in a wide range of downstream tasks, Uni-RNA achieved leading results (SOTA) in all seven major tasks in the three RNA domains: RNA structure prediction, mRNA sequence property prediction, and RNA function prediction, opening up endless possibilities for future in-depth research in the RNA field.

"Al for Life Sciences", a new paradigm that combines AI capabilities with underlying biological mechanisms, is injecting new vitality into the entire industry from the perspective of underlying technological breakthroughs, and its systematic development is expected to bring more possibilities to the industry.

Finding neoantigens is the core of personalized cancer immunotherapy. However, using traditional experimental methods to find precise recognition of T-cell-bound Peptides on the cell surface and then validate the "immunogenicity" is still a costly and time-consuming way. Li Ming, a fellow of the Royal Society of Canada and a professor at the University of Waterloo in Canada, said, "The specificity of neoantigens between individuals is extremely high, and it is difficult to get an accurate match in the general protein or peptide database, so de novo sequencing technology without a database reference requires a high degree of accuracy, and an efficient method is urgently needed to verify the neoantigens." The introduction of deep learning methods and the development of "neoantigen de novo sequencing" methods have brought new opportunities. Simulating the human central tolerance system to solve the problem of no TCR in immunogenicity prediction, using deep learning technology, the detection accuracy and efficiency of neoantigens are greatly improved. In the field of life sciences, artificial intelligence helps promote the combination of "wet experiments" and "dry experiments" to promote the landing and universal benefit of personalized cancer immunotherapy."

The use of AI technology for data analysis, model construction and prediction, as well as the development of computational tools, all demonstrate the great potential and broad application space of AI4S in the field of life sciences.

Zhang Dan, associate professor at Central China Normal University, said that "iron death" is a programmed way of cell death that is different from apoptosis, necrosis and autophagy. Catalase (CAT) is expected to be a new target to induce iron death in tumor cells, and the key to achieve this goal is to design and synthesize highly active CAT inhibitors. The results of animal experiments showed that BT-Br could effectively inhibit tumor growth in CRPC mice. This study suggests that CAT has the potential to be a new target for the treatment of CRPC based on iron death induction strategies. This set of processes can be applied to the field of plants for weeding or fighting fungal diseases, which is also the result of the application of artificial intelligence technology to the laboratory from calculation to synthesis, to molecular biology experiments and animal and plant experiments.

Today, the new paradigm of AI for Science has provided strong support for the field of life science and greatly promoted the innovation and development of the field of life science. With the continuous progress of AI technology, future life science research will be more intelligent and efficient, bringing more hope for human health and well-being.

On August 10-11, the 2023 Science Intelligence Summit was successfully held in Beijing. As a series of activities of the Zhongguancun Forum, the 2023 Science Intelligence Summit is hosted by the Beijing Institute of Science Intelligence, aiming to build a co-creation platform for scientific research breakthroughs, technology cultivation and talent exchange in the field of AI for Science. The summit set up a main forum and 10 thematic academic summits, covering topics such as model algorithms, databases, energy materials, and computing engines. At the meeting, participating academicians, experts and business representatives shared advanced ideas and cutting-edge insights, presented research results and innovative technologies, and looked forward to the future development trend of AI for Science.