The Zhitong Finance App learned that Northeast Securities released a research report stating that it focuses on the transformation of results and grasps the two main lines of commercial verification and clinical verification. Currently, AI pharmaceuticals are in the stage of deepening technology application to commercial implementation. It depends on pharmaceutical company cooperation and platform delivery in the short term, and clinical evidence and drug asset value release in the medium to long term. More worthy of attention are enterprises with cross-project replication capabilities, clear results rights, and reasonable capital investment. Focus on tracking the expansion of cooperation between large pharmaceutical companies, key clinical data, actual milestone payments, and cash flow improvements, and be wary of excessive extrapolation brought about by single license revenue and individual success stories.
The main views of Northeast Securities are as follows:
AI pharmaceuticals combine artificial intelligence with computational simulation and experimental verification to assist in target discovery, molecular design, property prediction, and formulation delivery optimization. It is an important direction of digital intelligence in pharmaceutical research and development
At present, the industry has formed major technology entry paths such as small molecule design, biopharmaceutical research and development, delivery and formulation optimization, and is gradually building a closed loop of “computational design - experimental verification - data feedback”. It is moving from single-point tool application to the stage of platform-based delivery and clinical verification.
Market space: R&D efficiency improvement needs are clear, and AI-related investment is growing at an accelerated pace
According to the Frost & Sullivan report, global drug development spending is expected to increase from US$260.5 billion in 2023 to US$394.3 billion in 2030, with a compound annual growth rate of 6.1%; global spending on AI-enabled drug research and development will increase from US$11.9 billion in 2023 to US$74.6 billion in 2032, with a compound annual growth rate of about 22.6%. The expansion of R&D investment and the need to improve efficiency jointly support the development of the industry. The actual revenue space of the platform depends on the pharmaceutical companies' external procurement, scope of services, and asset equity allocation.
Technology competition: basic models are becoming more popular, and differentiation shifts to a closed loop of data and experiments
Small molecule development focuses on multi-parameter optimization, biopharmaceutical development focuses on functional and developability design, and delivery formulations focus on material, internal distribution and process collaboration. As the threshold for basic tools falls, the focus of competition gradually shifts from single-point model performance to high-quality experimental data, interdisciplinary collaboration, and cross-project delivery. Whether proprietary data can continuously improve candidate quality and reduce experimental cycles has become an important basis for judging technical barriers.
R&D verification: There are cases of early efficiency improvement, and clinical transformation is still a key test
Drug candidates developed by AI are gradually entering human research, providing an opportunity for verification from computational power to clinical value; the termination or efficacy of projects such as DSP-1181 and BEN-2293 has not met expectations, which also indicates that AI cannot eliminate drug development risks. Existing studies have shown some early positive signs, but this is not enough to prove that AI has improved the clinical success rate of the entire process. In the future, we need to focus on observing multiple projects, long-term, and confirmatory research results.
Commercial cash out: Multiple models go hand in hand, and continuous charging and return on investment determine differentiation
The software model relies on workflow embedding and renewal, the service model relies on delivery reuse and cost control, and the asset model relies on pipeline quality and licensing benefits. Jingtai Technology, Insilicon Intelligence, Jietai Technology, and Hualan Co., Ltd. each reflect paths such as comprehensive R&D and automation, generative AI and drug assets, delivery formulations, and industrial resource integration.
Risk warning: Technical and clinical trials fall short of expectations, commercialization progress falls short of expectations