2228 JingTai Technology Visit Record and Thoughts 02.2

Crystal Technology's Core Technological Barriers:

1. Core advantages include globally leading algorithms, computing power, and high-quality databases: Crystal Technology has strong algorithm models and data foundations in the AI manufacturing field. The self-developed V^3 technology platform (Virtual in Vitro and in Vivo) covers three stages: intelligent molecular design, in vitro evaluation, and in vivo evaluation. It has accumulated cutting-edge technologies such as protein design, high-content screening (+AI), in vivo pharmacokinetics, and automated synthesis and screening of peptides to improve the efficiency and success rate of preclinical and clinical stage research and development, achieving multiple technological breakthroughs. Crystal Technology is currently the only company in the world that possesses two top predictive algorithms: 1) Crystal Technology is currently the only company in the world with two top predictive algorithms, demonstrating strong technical strength. Crystal Technology's molecular structure prediction algorithm won first place in the global crystal structure prediction blind test competition (CSP Blind Test) held by the University of Cambridge in 2014. This algorithm can accurately predict the crystal structure of molecules under different conditions, providing key theoretical support for drug development and helping experimental personnel design and screen drug polymorphs more efficiently. After ten years of continuous refinement and upgrades, in the seventh CSP Blind Test in 2024, Crystal Technology stood out from 28 participating teams worldwide with its outstanding capabilities and won first place again. 2) Crystal Technology's XtalFold protein structure prediction algorithm also has significant influence in the industry. This algorithm uses AI technology to model the structures of biomolecules, especially excelling in complex regions such as antibody-antigen interfaces. XtalFold has been adopted by several global pharmaceutical companies, including Janssen Pharmaceuticals (a subsidiary of Johnson & Johnson) and UCB, with the collaboration with Janssen focusing on validating small molecule lead compounds targeting specific sites, while the collaboration with UCB focuses on the discovery and engineering design of macromolecular drugs. These two algorithms have not only received high recognition in academia but have also achieved commercialization through cooperation with international pharmaceutical companies, laying a solid foundation for Crystal Technology's technological leadership in AI pharmaceutical development. The quality and speed of data accumulation are key to building large models in the field of chemistry and will be the core barrier for the next ten years: our automated and intelligent experimental system has achieved the accumulation of full-process data, including data from every link of feeding, shaking, reaction, testing, and analysis, and both successful data (positive data) and failed data (negative data) are recorded. This is very important data assets for us, and this data is accumulating rapidly and is used to train our chemical large model, which will predict synthesis reaction pathways and strategies. In drug and material development, a significant bottleneck is discovering excellent molecules that are difficult to synthesize, resulting in missed opportunities for innovative therapeutic molecules or high-value material molecules. Through the chemical large model, we will solve this bottleneck. This is an absolute barrier that others do not possess

2. The team has strong technical capabilities, strong management skills, and is youthful: On one hand, the team has a deep technical accumulation. The founding team comes from MIT, and the core team consists of outstanding talents from the IT and pharmaceutical industries. The company has had a solid technical foundation since its inception, with a higher proportion of R&D personnel than its peers, providing a first-mover technological advantage and subsequent R&D support. On the other hand, the founding team has strong management capabilities and the overall team is youthful.

3. Entering the field through crystal form prediction, leveraging small molecule compounds, and gradually deploying large molecule compounds: JingTai chose to enter through crystal form prediction, applying AI technology to the synthesis of small molecule compounds. Overall, the chemical structure of small molecule compounds has a clear advantage in computer representation. With Alphafold's precise prediction of protein structures and the Nobel Prize awarded to the inventors of Alphafold, JingTai has also deployed Xtalfold in the direction of large molecule drugs to predict protein complex structures. This platform has already been licensed for paid use by major pharmaceutical companies such as Johnson & Johnson and UCB.

4. Innovative cooperation methods have raised the company's profile by customizing an AI drug simulation platform for Pfizer and providing customized cooperation for several large biopharmaceutical companies: JingTai has gained recognition by providing customized drug development simulation platforms for several leading pharmaceutical companies, represented by Pfizer. By the end of 2024, JingTai Technology has collaborated with over 300 leading pharmaceutical companies globally, with 16 out of the top 20 pharmaceutical companies establishing partnerships with JingTai. On May 31, 2023, JingTai signed a $250 million (approximately RMB 1.77 billion) order for AI small molecule new drug discovery cooperation with Eli Lilly, becoming the highest single drug development pipeline collaboration amount for AI pharmaceutical companies in China.

5. High-quality data generated from collaborations with leading pharmaceutical companies accelerates the iteration and upgrading of technical models: High-quality cutting-edge first-hand data generated from deep collaborations with industry investment R&D institutions/pharmaceutical companies can contribute larger-scale and higher-quality training data for internal AI models, aiding in the iteration and optimization of AI algorithm models, improving the molecular screening, prediction, and analysis capabilities of the algorithm models, further strengthening the company's technological advantages.

6. The company has attracted significant attention in the capital market, creating financing records for Chinese AI pharmaceutical companies, with investors including Aobo Capital, China Biopharmaceutical, Sequoia Capital, Wuyuan Capital, Tencent Investment, SoftBank Vision Fund, CICC Capital, China Merchants International, CITIC Capital, SIG Haina Asia, and other well-known institutions.

7. Favorable policies and increased attention to AI+ applications: Currently, the innovative drug and artificial intelligence manufacturing fields align with policy directions. AI+ is a core industrial upgrade strategy of the government. Currently, countries around the world have clear strategic policies regarding the AI industry. These policies promote the development of artificial intelligence technology while providing broad development space for JingTai Technology. In 2024, the United States will launch an "AI Policy Roadmap," planning to invest $32 billion annually in non-defense AI innovation for R&D, covering high-end AI chip design, manufacturing, and a series of "AI Grand Challenges" programs Singapore released the "National Artificial Intelligence Strategy 2.0" in December 2023, aimed at promoting the widespread application of artificial intelligence in the economy and society. The global focus on AI+ applications in terms of technological maturity, innovation, and industry application has significantly increased JingTai Technology's recognition in the industry and market opportunities, accelerating its business expansion and diversification, gradually applying AI technology in fields such as new energy, new materials, and chemicals.

8. JingTai Technology has strong algorithm models, computing power, and data foundations in the AI manufacturing field. In the process of transforming into an AI intelligent manufacturing platform and entering sectors such as agriculture and energy, it has greater platform and scalability advantages compared to other companies in the same industry