AI-driven drug discovery company Deep Intelligent Pharma has secured nearly $15 million in Series B financing, with HongShan as the sole investor. Li Xing, Founder and CEO, stated that the funds will primarily be allocated to product development and business expansion in the field of AI-enabled pharmaceutical R&D. Previously, the company raised several million dollars in Pre-A financing from ZhenFund, followed by additional A-round investment from the same firm.
According to Li Xing, the company currently serves more than 50 collaborative clients, including top-10 multinational pharmaceutical companies, leading domestic pharmaceutical firms, and small and medium-sized domestic pharmaceutical enterprises. Cooperation models encompass SaaS services, on-premises deployment, outsourcing services, and strategic consulting. She emphasized that, unlike the commercial pathways of other AI-driven drug discovery companies, Deep Intelligent Pharma adopts a “market-fed R&D” strategy—namely, rapidly achieving commercialization to meet market demands, thereby supporting the research and development and product iteration of its other pipelines.
Therefore, regarding the commercialization of its products, Li Xing introduced that the intelligent pharmacovigilance system and registration submission translation services have already been commercialized, while the AI-driven early drug discovery platform and the AI-assisted organic synthesis system (for chemical synthesis and process improvement) will enter the commercialization phase this November.
Deep Intelligent Pharma tackles both ends of new drug development, leveraging AI to accelerate early-stage drug discovery and clinical research. Li Xing stated that future competitive advantages will primarily hinge on product quality, speed of implementation, and pricing. In terms of commercialization progress and product portfolio, Deep Intelligent Pharma has already established itself as a first-tier player in China’s AI-driven drug development sector. However, Li also emphasized that the drug R&D process involves approximately 20 functional modules; even experts specializing in one module typically possess only a general understanding of others. Particularly given that “pharmaceuticals remain an industry vastly distinct from IT and the internet,” this has created the greatest current challenge in the AI drug development landscape: the effective integration of pharmaceutical experts with AI engineers, or alternatively, the acquisition of cross-disciplinary talent combining industry expertise with technical skills. She noted, “Deep Intelligent Pharma’s talent structure is already highly comprehensive, with some pharmaceutical experts successfully transitioning into roles such as AI product managers, data mining specialists, or AI coaches.”