A：Jinnai Holdings is a AI for science company focused on empowering scientific research with AI, particularly in the biomedical sector. The highlight of the company in 2025 is that the main business is to promote the development of biomedical pipelines through AI technology, and 5 to 6 pipelines have entered the clinical or IND enabling stage. The number of customers increased 62% year-on-year, the cumulative amount of cooperation orders reached tens of billions of total package amount, 17 of the world's top 20 pharmaceutical companies are the company's core customers. Jinnai's main business layout is divided into two major areas: one is drug discovery solutions, providing end-to-end research and development process services for new molecules; The second is to AI for science intelligent solutions to help customers upgrade infrastructure, such as selling robots or providing intelligent research and development services.

A：Revenue from the drug discovery solutions business grew more than 400 percent year-on-year, with regular orders up 66 percent after excluding large orders in the eastern region. AF3 Solutions revenue grew more than 60 percent last year. Core customers include Johnson & Johnson, UCB and Pfizer.

A：In addition to biomedicine as the core business, Jinnai successfully expanded into the fields of new materials and consumer health last year, verifying the wide application potential of AI technology in these fields. At present, Jinnai has built more than 200 AI algorithm models, covering many industries such as chemistry, biology, drugs and materials, and has a self-developed multi agent system, which can independently promote tens of thousands of experiments every week.

A：Jinnai has core shareholders in early financing, including Internet companies such as Tencent, Google and Softbank, as well as financial investors such as Sequoia and National Life. The company has a global layout, with bases in Shenzhen, Shanghai, Beijing, Boston in the United States and Liverpool in the United Kingdom, of which Boston in the United States and China are mainly responsible for BD team, docking head MC. Jinnai has more than 1400 employees as a whole, with more than 70% of R & D personnel, and more than 500 doctoral and master's degrees or above. The four core competencies include AI model capability, robot laboratory, interdisciplinary expert team and data flywheel closed loop.

A：The three founders of Jinnai all come from the background of MIT quantum physics. The quantum physical mechanism they have mastered can study the interaction between atoms from the micro level, and is related to the physical and chemical properties of matter in the macro world, so as to be applied to the field of material discovery, not only in biomedicine, but also in the fields of new materials, new energy and consumer health.

A：Jinnai has established more than 200 AI models, covering the entire research and development process, and has a self-developed robot laboratory. There are currently more than 250 robots, and the data collection efficiency is 40 times that of manual labor. In addition, the company combines teams of experienced scientists to form interdisciplinary synergies.

A：The core barrier of Jinnai lies in its whole-process closed-loop system-DMTA process, which uses AI to design molecules, robot to synthesize and test molecules, and then feeds back to AI for training iteration and upgrading, as well as multi-platform drug research and development capability, including multi-modal drug platforms such as small molecules, macromolecules, polypeptides, molecular gels and nucleic acids, which are very scarce in the world.

A：AI-enabled drug discovery has the advantage of being more innovative, faster, and more accurate. AI models are able to generate novel and diverse molecules, which are significantly more innovative than traditional methods, and through active learning algorithms, we can screen out highly active molecules in less time, ten times faster than traditional virtual screening algorithms, and five times more efficient. In addition, AI can predict the feasibility of synthetic molecules with 90% accuracy, which is traditionally difficult for chemical experts to do.

A：Our multimodal drug discovery platform covers small molecules, molecular gels, antibody peptides and small nucleic acids. In terms of small molecules, we have reached a cooperation of nearly US $6 billion with an enterprise and have received the second payment of US $19 million. The cooperation is progressing smoothly. Molecular glue is developing and building a platform. Antibody development and platform authorization cooperation were signed last year. There are also platform authorization cooperation with Johnson & Johnson and UCB. The polypeptide field has made a milestone breakthrough, reached a cooperation with Gani Pharmaceuticals, and plans to declare the health care path of oral polypeptide products in the US FDA, which is expected to be approved soon. In addition, we plan to work with food and beverage companies to develop sugar-lowering molecules.

A：At present, the fastest is the pipeline of Higg Biotech, which focuses on diffuse gastric cancer. This year, it has completed the first clinical phase, and will enter the second clinical phase in the second half of the year. It has been nominated for the Nobel Prize-level introduction award, which is highly recognized by the biomedical industry. Another pipeline for solid tumor peptide inhibitors, has been approved by the US FDA M1, this year will also enter the clinical stage.

A：Many high-quality enterprises have been hatched in Jintai Ecology. For example, Jitai Technology was just listed in Hong Kong in May this year, focusing on AI delivery platforms, which is a benchmark case. Companies such as Higco and CD Technology have also made outstanding achievements in drug research and development and organ-like chips, respectively, and have received high attention from the industry. In addition, Jintai has also invested in AI cosine intelligent solution, using the advantages of AI training model to solve the problems of low efficiency, data missing and prejudice in traditional manual experiments, building a mechanical laboratory, improving the data collection efficiency by 40 times, and developing precision instruments such as smart spoon, which are applied to chemical experiment scenes to realize accurate sample addition below 5 mg.

A：The machinery laboratory has cooperated with a number of leading pharmaceutical and chemical companies in drug research and development, including South Korea's JW, Sinopec and BASF and other leading global chemical companies. It has performed well. Last year's orders were bright, and this year's orders were also very good. At the same time, the commercialization of the robot laboratory is also being actively carried out.

A：In the field of drug discovery, we adopt an end-to-end cooperation model, receiving down payments and project start-up fees, and delivering preclinical candidate compound molecules according to customer requirements, with corresponding milestone payments at the time of delivery, so as to cover research and development costs and achieve profitability.

A：Although companies such as OpenAI have entered the field of Chinese medicine AI pharmaceuticals, we have self-developed AI models in this field, such as the s to four model, which have better prediction accuracy in the antibody field than Google's alpha model and are paid for by companies such as Johnson & Johnson and UCB. Therefore, even in the case of open source, our platform still has a competitive advantage. In the early days, the company focused on a platform model, reducing customer research and development risk by providing AI and robotic platform services, while also receiving milestone payments to form a stable cash flow and low-risk business model. As AI and robotic platforms mature, we begin to add self-research pipelines to produce high-quality molecules at scale, thereby reducing the risk of entering the clinical phase, and enhancing intelligence through self-research pipeline iterative models.

A：At this stage, our AI and robotics platform is relatively mature and scalable, and the self-research pipeline is able to generate data autonomously, further iterate and optimize the model, and reduce the overall research and development risk.

A：Due to the long R & D cycle and low success rate of the biomedical industry, it is difficult to cover all fields and do well with only one company. Therefore, through investment and incubation of collaborative enterprises such as Jitai, we can make up for our shortcomings in the industrial chain and achieve complementary advantages.

A：We feel that the biggest strengths and challenges are in the data. The biggest advantage is that we have a wealth of industry data, including data accumulation in the field of drug discovery and high-quality data generated by robotic laboratories. At the same time, our cooperation with large pharmaceutical companies such as Pfizer also provides us with valuable data support. The main challenge in the future is how to obtain more high-quality data, such as conquering chemical synthesis reactions that have not been covered by the mechanical laboratory, and accumulating clinical experimental data.

A：Our advantages in data are reflected in many aspects: first, through cooperation with Pfizer, we have successfully entered the drug discovery track and accumulated a large amount of valuable drug discovery data in this field; Secondly, our robot laboratory is the world's leading in scale and technical accuracy, and can generate high-quality data of positive and negative samples 24 hours a day. Finally, our three founders have quantum physics background, the ability to generate data using deterministic equations and, combined with years of deep industry knowledge and know-how, turns some data that may be considered invalid into valid data that can be used for AI model training.

A：We have long-term strategic partnerships with a number of large pharmaceutical companies such as Pfizer, including Pfizer as the first major customer, although the amount of a single cooperation is relatively small, but has brought long-term stable business endorsement. The entire market space is huge. The market size of the biomedical field alone has reached the trillion level. As a company that provides AI services, our market share is small, but we also have a market space of 100 billion. In addition to biomedicine, there are new materials and consumer health. For the specific scale of cooperation and the scale of the company's development, due to the involvement of multiple customers and different stages of cooperation, it is impossible to simply give an exact value, but it is certain that with the in-depth cooperation with more customers and the advancement of pipeline projects, The company's overall scale and economic scale will continue to grow.