Damo Data Secures New Funding Round to Accelerate AI-Powered Drug Discovery Platform

Recently, Damo Data announced the completion of a new round of financing. The investor, Xiao Ying, is the founder of a well-known publicly listed internet company and is currently focused on the development of the medical, healthcare, and elderly care industries. This round of financing will be used to further develop Damo’s AI-driven drug R&D data platform, building new digital infrastructure for AI-enabled drug discovery across three dimensions—computing power, algorithms, and data. The platform will serve global pharmaceutical companies, biotechnology firms, and partners, addressing the urgent need for improved efficiency and accelerated growth in the pharmaceutical industry.

Damo Data is committed to integrating artificial intelligence with multidisciplinary fields such as pharmacy, chemistry, and biology, building an AI-powered drug R&D data platform, and leveraging big data and AI technologies to empower pharmaceutical companies in their drug development processes.Currently, Damo Data has completed the construction of knowledge graphs for over 30,000 small-molecule drugs and biologics, with data volume reaching the petabyte (PB) scale. In the near future, several key drug R&D modules, including retrosynthesis prediction and drug screening, are set to be deployed, further strengthening the company’s data advantage in the field of drug research and development.

Viewed from the perspective of the entire industry, new drug development faces significant pain points: high investment, long timelines, and low success rates. On average, it takes 10–15 years to develop a new drug from project initiation to regulatory approval for market launch, with R&D costs amounting to approximately $2.6 billion. Since 2010, the return on investment in R&D has shown a declining trend year by year.

How to improve the speed and success rate of new drug development is a question that the entire pharmaceutical industry has been continuously focusing on. The digital wave, led by AI and big data, has undoubtedly ignited a spark for the development of this industry.

Drug development, spanning project initiation, target identification, compound screening and synthesis, validation and optimization of active compounds, pharmacological, toxicological, and pharmacokinetic studies, formulation development, clinical trials, and market launch, requires the collection and organization of industry data, drug information, drug-related patents, and other materials, a process that often takes several years. Throughout the R&D process, companies must also investigate scientific research data across dozens of major categories, including the number of companies and projects under development in the field, R&D stages and structural characteristics, synthetic routes, preclinical trials, market sales, patent landscapes, and literature.

In this field, the Damo team demonstrates a strong internet DNA, with profound expertise in big data and AI technologies, giving it a distinct advantage in building data platforms. It is understood that the founding and executive teams of Damo Data possess extensive experience in internet product algorithm iteration and drug R&D.

AnswerMagic Data founder Zhang Yu graduated from the PBC School of Finance at Tsinghua University and previously worked at Tencent; other core team members graduated from a series of prestigious domestic and international institutions, including MIT, Tsinghua University, Peking University, and Sichuan University, and have gained experience at renowned companies such as Tencent, Merck & Co., AstraZeneca, and WuXi AppTec.

The internet DNA of the Damo team has fostered a style characterized by strong execution, rapid product launches, and swift iterative upgrades. Furthermore, Damo Data’s expert advisory team consists entirely of founders of publicly listed companies in the medical sector. They have provided forward-looking guidance on various aspects of Damo’s development, propelling the company onto a fast track for growth.

Damo Data’s knowledge graph is structured around active pharmaceutical ingredients (APIs), targets, companies, and indications. It covers more than 30,000 small-molecule drugs and biologics that are globally marketed, under clinical trial application, or in the research and development stage. The graph encompasses comprehensive R&D data across all domains, including drug activity data, target information, clinical trials, regulatory approval, consistency evaluation, marketing and sales data, formulations and excipients, process data, pharmacopoeias, drug labels and the Orange Book, guidelines and reports, as well as patents and literature.

Currently, Damon Data has secured orders from dozens of benchmark key clients in the industry and completed delivery; the company has also established strategic partnerships with several well-known pharmaceutical companies in China, working alongside industry experts to deeply explore the application of artificial intelligence in drug research and development.

Founder Zhang Yu is highly optimistic about the practical application of AI technology in pharmaceutical R&D. He believes that new technologies will bring high-efficiency solutions, which are conducive to opening up blue-ocean markets. “In this productivity revolution, DaMo is among the early enterprises to effectively integrate pharmaceutical big data with AI algorithms, having fully optimized its computational architecture and established a drug data iteration system from an engineering perspective. The successful full-scale delivery for dozens of benchmark key clients has preliminarily demonstrated the feasibility and scalability of our technology. We believe that DaMo Data will become a new generation of critical infrastructure in the new drug development industry in the near future.”