DP Technology raises $114M to accelerate AI for science
Updated: December 24, 2025, 19:48 EST
Beijing-based DP Technology Co. Ltd. secured 800 million yuan ($114 million) in Series C funding to push its AI tools deeper into scientific research-spanning battery design, molecular simulation, and drug discovery.
The round was led by Fortune Venture Capital and the Beijing Jingguorui Equity Investment Fund, with additional backing from the Beijing Artificial Intelligence Industry Investment Fund, Beijing Pharmaceutical and Health Industry Investment Fund, Lenovo Capital, Incubator Group, and Oriza Hua.
What DP Technology has built
Founded in 2018 by Peking University alumni, the company focuses on "AI for science" with a suite that targets core steps of the research pipeline. The tools are already in use across academia and industry, aiming to compress iteration cycles and reduce manual overhead.
- Particle Universe: AI-driven simulation models for physics and chemistry workloads.
- Uni-Lab: A laboratory operating system for orchestration and automation.
- Bohrium: Cloud-based research hub for compute, collaboration, and workflows.
- Hermite: Computer-aided drug design platform.
- RiDYMO: R&D platform targeting "undruggable" biology.
- Piloteye: Battery design tooling for materials and cell architectures.
- SciMaster: General-purpose scientific AI agent for research tasks.
- Lebesgue Intelligent Computing: Controls and optimizes network traffic for scientific workloads.
Traction and credibility
According to co-founder and Chief Scientist Zhang Linfeng, DP Technology's tools are used by 1,000+ universities and research organizations and 150 corporate clients worldwide, including PetroChina and BYD.
Zhang received the ACM Gordon Bell Prize in 2020 for work that advanced machine learning methods in molecular dynamics simulations. He co-founded the company with Chief Executive Sun Weijie, who set a long-term goal of building "AI scientists" capable of automating discovery.
"AI for Science is more than just an emerging sector - it is a foundational infrastructure project for decades of scientific discovery to come," Zhang said.
Why this matters for research teams
- Simulation throughput: Particle Universe points to faster, more accurate molecular and materials studies-useful for exploring parameter spaces that would be impractical with classical methods alone.
- Lab execution: Uni-Lab suggests a path to consistent protocols, instrument coordination, and auditability-key for scale and reproducibility.
- Drug programs: Hermite and RiDYMO target candidate generation and screening, with potential to expand the tractable space around "undruggable" targets.
- Energy R&D: Piloteye focuses on battery chemistries and architectures, narrowing design loops for materials teams.
- Operating model: SciMaster hints at agentic workflows-triaging literature, drafting experiment plans, and assisting with analysis.
What to watch next
- Integration: How cleanly Uni-Lab plugs into existing LIMS/ELN, instrument drivers, and data lakes will determine time-to-value.
- Validation: Benchmarking against gold-standard datasets and prospective studies will matter more than demos.
- Compute: Clarity on on-prem HPC vs. cloud usage, costs, and data residency-especially for pharma and energy clients.
- Governance: IP boundaries, model provenance, and audit trails for regulated work.
- Talent: The company plans to hire; labs will need skills in ML ops, scientific modeling, and automated experimentation to match.
Funding use and near-term outlook
DP Technology plans to use the capital to hire and speed up development across its product suite. For teams considering adoption, start with a narrow, high-impact workload-e.g., a single assay class, a defined battery materials screen, or a focused molecular dynamics benchmark-and set clear success criteria before scaling.
If you're building internal capacity for AI-driven R&D, you may find targeted training helpful: AI courses by job role.
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