According to a report by the Science and Technology Innovation Board Daily on December 12, the Center for Global Health Drug Discovery (GHDDI) and the Center for Scientific Intelligence at Microsoft Research announced that they have reached a cooperation to jointly develop generative artificial intelligence and basic large model technologies in the field of global health infectious diseases, focusing on implementation and transformation, and accelerating the research and development of innovative drugs. Previously, the two parties have successfully designed a variety of small molecule inhibitors with novel structures in the study of Mycobacterium tuberculosis and key target proteins of coronavirus.

PANews reported on December 11 that, according to Molecule's official news, Sora Ventures has invested $1 million in Molecule, a strategic investment aimed at reshaping the scientific research landscape in Asia, with the goal of transcending physical boundaries, promoting a culture of objective science and intellectual property sharing, and making full use of the advantages of decentralized science (DeSci). To foster innovation in the field of biomedical research, Molecule will provide grants specifically to researchers in Asia, with plans to award $50,000 to deserving scientific teams or individuals.

According to official news, Molecule announced that it has received a $1 million investment commitment from Sora Ventures. This strategic investment aims to expand DeSci's integration in the Asian market, with the goal of transcending physical boundaries and fostering a culture of objective science and intellectual property sharing, leveraging DeSci's decentralized nature.

According to 36Kr, on November 28, Shuimu Molecule, the start-up team of Tsinghua University's large model, revealed that Xing Jie, former vice president of WuXi AppTec, has officially joined and will serve as the chief operating officer, responsible for promoting the global commercial development of Shuimu molecules, ecological construction and other affairs.

According to a report by Shangguan News on November 1, at the 2023 Apsara Conference, Fudan University's intelligent computing platform CFFF released major progress in the fields of meteorology, chemical molecule pre-training, medical and other scientific large models. Qi Yuan, dean of the Institute of Artificial Intelligence Innovation and Industry of Fudan University, said that in addition to the meteorological model, other scientific models trained based on the CFFF platform have also made significant progress in the past few months. Among them, the large model of chemical molecule pre-training can predict the druggability and safety of candidate compounds. The large medical model with a scale of 100 billion parameters is deeply customized for medical scenarios, forming medical AI professional capabilities covering the whole process and multiple scenarios of medical treatment, which can help intelligent diagnosis and treatment.

According to Xinzhiyuan, on November 1, Google's DeepMind announced the latest progress of AlphaFold - AlphaFold-latest. According to DeepMind's latest technical report, the next-generation AlphaFold is capable of more than just processing and predicting protein structures with greater accuracy. It can also generalize similar capabilities to other biomolecular structures such as nucleic acids, arbitrary small molecule ligands, and so on. According to DeepMind, the expanded functionality and performance of the new model can accelerate biomedical breakthroughs, opening up new possibilities for disease pathways, genomics, biorenewable materials, plant immunity, potential therapeutic targets, and drug design mechanisms.

According to The Paper, the second "Global Development and Health Communication Forum" hosted by the School of Journalism and Communication of Tsinghua University was held at Tsinghua University from October 28 to 29. Dr. Rumin Zhang, Chief Scientific Officer of the Center for Global Health Drug Discovery, delivered a speech on the theme of "Using Artificial Intelligence to Overcome the Problems of Drug R&D". Zhang Rumin said that artificial intelligence can make a difference in four major aspects in the pharmaceutical field. The first is target identification: AI can do some analysis from the massive literature and internal key data, and use the human mind to do efficient and rapid analysis to recommend candidate targets. The second is to engage in virtual screening: from hundreds of millions of compounds, through rapid computer calculations, the compounds that are most likely to effectively bind to the target are screened. The third is drug design: based on the existing structure of the interaction between the compound molecule and the target, as well as the known structure-activity and configuration relationship, a more effective new molecule is designed. Finally, there are clinical trials: patients who are most likely to benefit from the designed drug are recommended based on known efficacy assumptions and biomarkers.

According to Xinhuanet, on September 20, the 2023 World Manufacturing Conference co-sponsored by the Ministry of Industry and Information Technology, the State-owned Assets Supervision and Administration Commission of the State Council, the Chinese Academy of Engineering, the Anhui Provincial People's Government, etc. was held in Hefei. Tsinghua University Intelligent Industry Research Institute (AIR) Distinguished visiting professor Xu Jinbo showed up with NewOrigin (Chinese name "Darwin"), a self-developed large-scale AI protein production model. "Molecular Heart is working with cooperative pharmaceutical companies to solve key problems in protein vaccine stability based on the NewOrigin large model." Xu Jinbo revealed. The NewOrigin large model can help drug research and development teams quickly analyze, locate and decompose the problems of improving the effectiveness of protein vaccines into sub-problems that can be solved based on AI methods, such as using AI to design mutations to improve the stability of protein vaccines. Then, NewOrigin will conduct mutation design on the wild-type protein, and automatically call a series of methods such as language model, evolution model, structural model, and energy model to extract protein features, predict the properties of the protein vaccine mutant, and obtain the most ideal protein molecule. . In just 3 days, NewOrigin can design dozens of ideal candidate proteins.

According to the "Kechuangban Daily" report on August 23, recently, Shuimu Molecule completed tens of millions of seed round financing. Shuimu Molecule is a developer of large-scale biomedical basic models, dedicated to building basic large-scale models of the biomedical industry and a new generation of conversational biomedical research and development assistants. The company's products will serve all aspects of drug research and development, including early research projects, target discovery, molecular design optimization, clinical trial design, drug repositioning, etc.

According to the news from IT House on August 18, this morning, the large-scale model start-up company Shuimu Molecule and the Institute of Intelligent Industry (AIR) of Tsinghua University announced that the open-source and commercially available multi-modal biomedical 10-billion-parameter large model BioMedGPT-10B. According to reports, the large model can be used to improve the efficiency of all aspects of drug research and development, including new drug project approval evaluation, drug design and optimization, clinical trial design, and indication expansion. The model's question answering ability in the field of biomedicine is known as "on par with human experts". It has achieved SOTA on multiple biomedical question answering benchmark datasets and has successfully passed the US Physician Qualification Examination. In addition, the team launched BioMedGPT-LM-7B together, claiming to be "the first commercially available Llama2 model dedicated to biomedicine".

According to a report by China Business News on August 15, Huada Gene said in a recent survey that in terms of future technological change trends, the company will focus on the application of AI technology in the multi-learning big data and synthesis business, and continue to invest in the research and development of single-molecule sequencing technology. Clinical application of new technologies such as spatio-temporal omics technology.

According to the "Kechuangban Daily" report on June 27, AI pharmaceutical company Insilico Medicine (Insilico Medicine) announced that the company's self-developed anti-fibrosis small molecule drug candidate INS018055 was approved to carry out the second phase of the international multi-center in China and the United States. Clinical trials, and completed the first batch of patient dosing. This marks that the world's first drug candidate whose novel target discovery and molecular design is completed by generative artificial intelligence has advanced to the phase 2 clinical trial verification stage.

According to news from Jinshi on June 26, Yunnan Baiyao stated on the interactive platform that the company had previously signed the "Comprehensive Cooperation Agreement on Artificial Intelligence Drug Research and Development" with Huawei Technologies Co., Ltd. The two parties will carry out extensive exchanges and cooperation in the field of artificial intelligence drug research and development, explore the mechanism of joint scientific research and innovation, and expand the breadth and depth of cooperation between the two parties, including but not limited to large and small molecule design, related diseases, database development, etc.

According to a report by the Financial Associated Press on June 2, the Shanghai Municipal Science and Technology Commission issued the "Shanghai Computational Biology Innovation and Development Action Plan (2023-2025)", which mentioned that basic research around algorithm development, model construction, and AI drug design System layout with applied basic research. Develop original algorithms, models and computing platforms for AI drug research and development, develop large models and generation algorithms for small molecule drugs, establish artificial intelligence-enhanced large models and design platforms for antibody drugs and peptide drugs, and develop pharmacological and pharmacodynamic prediction models for Chinese medicine compounds. Develop modern compounds with synergistic effects.