StoneWise Secures Nearly $10 Million in Series A Funding to Advance Dual AI Platforms for Early-Stage Drug Discovery

On March 16, StoneWise (HK) Limited (hereinafter referred to as “StoneWise”) announced that it had recently completed a Series A financing round of nearly $10 million, jointly invested by the well-known US dollar funds Long Hill Capital and Linear Capital, with Mingxi Capital serving as the financial advisor for this financing.

Mr. Zhou Jielong, Founder and CEO of StoneWise, stated that the funds raised will be primarily used to continuously optimize the StoneWise technology platform and drive technological innovation, strengthen core product development, enrich database resources, advance strategic partnerships and business expansion, and attract more outstanding managerial and technical talent.

StoneWise, established in 2018, is an AI-driven technology company dedicated to new drug R&D, committed to building a globally leading platform for innovative drug discovery and development. With technological R&D as its core driving force, StoneWise’s AI platform has already demonstrated significant technical advantages in early-stage drug discovery, including drug knowledge mining, identification and selection of privileged scaffolds, molecular generation and design, and property prediction.

Mr. Zhou Jielong, Founder and CEO of StoneWise, previously served as Chief Architect at Baidu, where he led the company’s core team in integrating and applying artificial intelligence to search, successfully restructuring Baidu Search. Driven by a strong sense of mission, Mr. Zhou entered the field of drug discovery in 2017 and has been exploring this domain for nearly three years. He is one of the few experts with profound insights into both drug discovery and artificial intelligence.

“AI-driven drug discovery is, in a sense, quite similar to search engines. Specifying a target is akin to providing a query. The problem we aim to solve is how to leverage artificial intelligence to navigate through 10⁶⁰"identify effective molecules within the chemical space of this magnitude," said Zhou Jielong.

Recently, AI-driven drug molecule discovery technology was named one of the “Top 10 Breakthrough Technologies of the Year” by MIT Technology Review, sparking widespread attention within the industry. According to MIT Technology Review, the average cost of commercializing a new drug is approximately $2.5 billion, partly due to the difficulty in identifying promising drug candidate molecules.

This is precisely the original aspiration and core competitiveness behind the establishment of StoneWise. For medicinal chemists, how to start from 10⁶⁰Identifying suitable molecules from a vast pool is a highly challenging task, whereas machine learning tools enable the discovery of new drug candidates at faster speeds and lower costs. The StoneWise team believes that the key to new drug development lies in the matching between targets and molecules; by computationally recommending optimal molecules with high druggability potential, search ranking serves as the core of this process.

“Many AI companies are accelerating their deployment in the early discovery of drug molecules, but integrating these technologies into the pharmaceutical industry chain is not a simple process,” said Zhou Jielong. He emphasized that one of the keys to ensuring deep integration between AI and the industry is to maintain algorithmic superiority based on a thorough understanding of chemical and biological principles. Continuous improvement at the level of underlying algorithms is essential to truly help drug R&D professionals discover advantageous scaffolds and drug molecules more rapidly.

To this end, StoneWise has been continuously dedicated to researching artificial intelligence algorithms such as reinforcement learning and autoencoder networks since its inception. By integrating knowledge from computational chemistry and computational biology, the company has deeply cultivated the application of AI technologies in new drug development. Driven by technological R&D, StoneWise focuses on leveraging AI to accelerate the process from early-stage screening of innovative drugs to the discovery of clinical candidate compounds. In practice, the company has successfully developed and deployed its first-generation AI technology platform, which features a robustly constructed complex system architecture based on big data processing, the combined application of multiple algorithms, and the integration of computational and quantum chemistry.

In just over a year, StoneWise has developed two core products: an AI-powered intelligent molecular design platform and a knowledge graph, covering stages from new drug discovery to preclinical research. The intelligent molecular design platform provides medicinal chemistry experts with functionalities for scaffold-based novel molecule generation and recommendation, as well as property-based molecular optimization, enabling the intelligent design of molecular structures. The knowledge graph extracts and integrates information on diseases, targets, small molecules, and more from billions of diverse data points across multiple reliable sources, thereby constructing a pharmaceutical knowledge graph database.

The data accumulated by the knowledge graph platform will provide data support for the molecular design platform, which can interact effectively with experts both within and outside the enterprise to further collect data and enrich the database of the knowledge graph platform, thereby creating a snowball effect.

Leveraging its proprietary molecular screening platform, StoneWise has established in-depth collaborations with listed innovative pharmaceutical companies in China and the United States, as well as leading research institutions, in areas such as virtual drug screening and rational drug design. Its partners include BeyondSpring, Peking University Health Science Center, and the Institute of Materia Medica at the Chinese Academy of Medical Sciences & Peking Union Medical College (some partners are not listed due to confidentiality agreements). The collaborative projects span First-in-class, Fast-follow, and Me-better drug development.

Furthermore, StoneWise is continuously researching and improving the underlying algorithms in this interdisciplinary field to build higher technical barriers and become a global leader in AI-driven drug discovery.

As the cumulative data in drug development grows rapidly, leveraging AI technologies to mine and process vast amounts of pharmaceutical data and structured knowledge has become an essential pathway for the pharmaceutical industry to accelerate its digital transformation in drug R&D. In recent years, an increasing number of large multinational pharmaceutical companies (such as Johnson & Johnson and Novartis) have actively partnered with AI firms for new drug development, while financing and collaborations for startups have reached historic highs. This trend suggests that the “AI + Healthcare” market holds immense potential for future growth.

In the race to integrate AI into new drug development, StoneWise is emerging as a significant new force. The company initially focused its market expansion efforts on China and North America, with plans to gradually expand globally thereafter. Currently, StoneWise operates and conducts R&D simultaneously in both China and the United States.

“AI-powered new drug development finds its most direct solution in leveraging AI technologies to discover promising lead structures and candidate drugs,” explained Dr. Liu Zhenming from the State Key Laboratory of Natural and Biomimetic Drugs at Peking University, a partner of StoneWise. He noted that molecule-skeleton-based drug discovery solutions, which involve learned ring-system scaffold hopping and the generation of novel molecules with new connectivity patterns, help overcome existing patent protections during the drug design process.

Meanwhile, the results of molecular generation enable researchers to strategically plan the claims for future patent applications at the very outset of derivative synthesis design. The team has co-authored a related paper with StoneWise in the Journal of Chemical Information and Modeling, published by the American Chemical Society, and has signed a strategic cooperation agreement.

To strengthen its team capabilities, StoneWise is continuously recruiting core technical and managerial talent in both China and the United States. In February this year, Dr. Yingsheng Zhang joined the company as Vice President of Drug R&D at StoneWise. Dr. Zhang earned his Ph.D. in Organic Chemistry from the University of Wisconsin and subsequently conducted postdoctoral research on oncology drugs at Harvard Medical School. Upon entering the industry, he served as a Senior Scientist at Merck and as Director of Medicinal Chemistry and CADD at Cytrx. He also co-founded several new drug development companies. Dr. Zhang has extensive experience advancing multiple projects from early-stage R&D to clinical and even late-stage clinical development in the fields of oncology, diabetes, obesity, antithrombotics, and non-alcoholic fatty liver disease (NAFLD). With over 20 years of experience in new drug development and project management, Dr. Zhang’s appointment will accelerate the integration of StoneWise’s leading AI research achievements into the drug discovery process, driving technological transformation in new drug development.

In addition, StoneWise has assembled a team of domestic and international experts and top talents from renowned pharmaceutical companies, leading AI firms, and internet enterprises, all united by their passion for life sciences. Over 90% of the core team members hold master’s degrees or higher. The technical team possesses core expertise and extensive engineering experience in AI algorithms such as machine learning, deep learning, and reinforcement learning, as well as in technologies like big data and cloud computing. The pharmaceutical R&D team has comprehensive experience and capabilities across the entire drug discovery process, including project initiation, computational chemistry, computational biology, quantum chemistry, structural biology, organic chemistry, medicinal chemistry, and preclinical project management. Consequently, the team brings highly professional and extensive expertise to identifying the most promising druggable molecules.

Regarding this financing round, Long Hill Capital, the lead investor, stated that the drug development industry possesses strong characteristics of a “traditional industry,” requiring extensive accumulation of experience. However, to develop newer and more effective drugs, it is necessary to continuously challenge conventional scientific and technological paradigms. The application of AI technology in drug development can significantly enhance efficiency in key early-stage processes of traditional drug R&D, breaking through limitations imposed by conventional workflows and industry organizational models. The StoneWise team boasts substantial technical reserves and cross-disciplinary expertise, demonstrating a profound understanding of both technology and the industry. We look forward to collaborating with more pharmaceutical companies worldwide to explore and achieve faster, lower-cost new drug development, thereby benefiting both the industry and patients.

Linear Capital has stated that AI-driven drug discovery is a key investment focus for the firm. While the costs and timelines associated with drug development continue to rise, success rates are declining. The advanced capabilities of AI in solving complex problems enable it to play a role across various stages of drug development, including the efficient and accurate identification and screening of candidate compounds, as well as accelerating clinical trial processes, thereby enhancing the overall efficiency of new drug development. Meanwhile, pharmaceutical companies have adopted a highly positive stance toward AI, demonstrating a strong willingness to pay for such solutions. AI-assisted drug development is expected to become a mainstream trend within the next two to three years.StoneWise’s team comprises senior AI experts and medicinal chemistry specialists who have accumulated extensive experience in AI and innovative drug development. CEO Zhou Jielong has applied his years of expertise in data and algorithms to the field of drug development. Combined with his passion and dedication to AI-driven drug discovery, this demonstrates significant potential. Many team members possess interdisciplinary backgrounds in both pharmaceuticals and computer science, making StoneWise a rare and valuable AI drug discovery startup team in China. After one year of development, StoneWise’s technology has already gained recognition from some clients. We are confident that StoneWise can help customers improve their drug development efficiency.

“We are deeply grateful for the support from Long Hill Capital, Linear Capital, and SIG, our existing angel-round shareholder. By leveraging Long Hill Capital’s extensive resources in the pharmaceutical sector, Linear Capital’s investment expertise in data intelligence, and SIG’s strategic guidance, StoneWise will build upon its ongoing collaborative projects—spanning First-in-Class, Fast-Follow, and Me-Better candidates—and, through iterative enhancements of its technology platform, ultimately realize the AI-driven innovation of drug discovery across broader domains,” stated Zhou Jielong. He added that StoneWise will remain grounded yet visionary, committed to continuous exploration and innovation in new drug R&D, thereby delivering unparalleled technology, products, and services to its clients.

Long Hill Capital, established in 2016, is a new generation of theme-driven venture capital fund. In the fields of big health and aging, Long Hill Capital is committed to investing in technology-driven business model innovations, making longevity a gift to every individual’s life in China’s rapidly aging society. The founding partners of Long Hill come from NEA, one of the largest venture capital funds in the United States, and were responsible for NEA’s investments in China from 2005 to 2015. Long Hill Capital has offices in Shanghai and Beijing, with its team collectively possessing decades of investment and operational experience. Currently, the total assets under management exceed RMB 3.5 billion, with investors including endowment funds of globally renowned universities and hospitals, foundations, pension funds, top-tier fund-of-funds, and other first-class institutional investors.

Linear Capital is a professional investment firm focused on data intelligence and frontier technology. Currently, Linear Capital is raising its fourth fund, with the total assets under management in US dollars and Renminbi equivalent to approximately RMB 3 billion. We primarily focus on early-stage projects in the fields of data applications, data infrastructure, and frontier technology. To date, we have invested in over 70 startup teams, including Horizon Robotics, Rokid, Tongdun, Kujiale, Sensors Data, and Tezign, with the combined valuation of our portfolio companies reaching approximately USD 11 billion. Linear Capital is striving to become China’s premier applied data intelligence fund and is progressively building itself into the most influential frontier technology fund.