DP Technology raises US$114 million to scale its AI-for-Science stack
DP Technology has secured more than 800 million yuan (US$114 million) in Series C funding to deepen R&D and recruit talent. The Beijing-based AI-for-Science company builds tools used across drug design, materials Research, and lab operations. The plan is simple: expand the team and push product development forward.
Who backed the round
- Fortune Venture Capital
- Beijing Jingguorui Equity Investment Fund
- Beijing Artificial Intelligence Industry Investment Fund
- Beijing Pharmaceutical and Health Industry Investment Fund
- Lenovo Capital and Incubator Group
- Oriza Hua
What DP Technology builds
Founded in 2018 by two Peking University alumni, DP Technology focuses on AI and advanced computing for fundamental research, life sciences, and materials science. The tools span molecular simulation, computational Design, and laboratory automation. The aim is to shorten iteration cycles and create tighter feedback loops between models and experiments.
- Particle Universe: AI model system for scientific simulation.
- Bohrium: Cloud-based research platform and asset hub.
- Uni-Lab: Laboratory operating system for day-to-day workflows.
- Hermite: Computer-aided drug design platform (see CADD background).
- RiDYMO: R&D platform for previously "undruggable" biological targets.
- Piloteye: Battery design platform for materials and cell architectures.
- Lebesgue Intelligent Computing: Computing traffic controller for resource allocation.
- SciMaster: General-purpose scientific AI agent that integrates with the company's stack.
Why this matters for your lab
An integrated stack across simulation, candidate design, and lab execution can reduce handoffs and data loss. If you're running multi-modal programs (chemistry, biology, materials), the shared data backbone and compute control may help standardize workflows and cut repeat work.
Two practical angles stand out. First, compute orchestration (via Lebesgue) paired with model systems (Particle Universe) suggests lower queue times and better utilization. Second, "undruggable" target tooling (RiDYMO) signals a push into structure- and dynamics-aware methods that could widen your screening funnel.
Practical next steps for teams evaluating DP Technology
- Ask about APIs, data schemas, and LIMS/ELN integration with Uni-Lab and Bohrium.
- Clarify data governance: on-prem vs cloud, encryption, audit, and IP boundaries for model training.
- Benchmark model components against your baselines for docking, MD, and generative design.
- Map compute needs early-GPU tiers, job scheduling policies, and expected throughput.
- Plan hiring around the stack: ML research engineer, scientific software engineer, and lab automation specialist.
What to watch next
Expect hiring announcements and updates across their simulation, drug design, and lab OS lines as the funding is deployed. For teams exploring adoption, look for case studies in pharma, energy storage, and advanced materials, plus clarity on enterprise features and support levels.
If you're upskilling your team for AI-enabled research, consider the AI Learning Path for Data Scientists as a focused route for training relevant skills.
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