Beijing-based DP Technology has secured 1 billion RMB (approximately $114 million) in Series C funding, a significant capital injection that underscores the growing investor appetite for “AI for Science”—the application of deep learning to hard physics and chemistry problems.
The round was led by Fortune Venture Capital, with backing from Lenovo Capital and Incubator Group, the Beijing AI Industry Investment Fund, and other institutional investors. The raise brings the seven-year-old startup’s valuation and war chest to a level where it must now transition from academic validation to industrial-grade deployment.
DP Technology operates in a sector that is significantly harder to crack than generative text or image creation. Its primary mandate is to shorten the R&D lifecycle for two of the most capital-intensive industries on the planet: pharmaceutical drug discovery and battery material design.
We don’t just want to speed up experiments. We want to change how discovery itself is done.
Building the Operating System for Science
Founded in 2018 by alumni from Peking University, the company emerged from a specific frustration: the computational bottleneck in traditional scientific simulation. While researchers could hypothesize new molecules, validating them via traditional physics-based simulations or wet-lab testing remained prohibitively slow.
DP’s thesis is that AI models, trained on quantum chemistry data and molecular structures, can predict material properties with high accuracy before physical testing begins. The company has productized this thesis into two main platforms:
- Hermite: A computer-aided drug design platform that utilizes pre-trained models for molecular property prediction and binding affinity estimation.
- Piloteye: A materials development platform focused on the energy sector, specifically for battery design and optimization.
The core technology blends machine learning with molecular dynamics, attempting to solve the “many-body problem” in quantum mechanics significantly faster than traditional supercomputing methods.
Scale-up over Survival
Unlike many AI startups currently scrambling for bridge rounds, this Series C is classified as a scale-up raise. The company has already moved past the prototype phase and is integrating its software into the workflows of pharmaceutical giants and battery manufacturers.
The $114 million will be directed toward three specific operational goals:
- Talent Acquisition: Aggressively hiring engineers and application scientists to bridge the gap between code and chemistry.
- Compute Infrastructure: expanding the high-performance computing clusters required to train increasingly complex models.
- Commercial Integration: Hardening their software for enterprise environments where IP security and regulatory compliance are non-negotiable.
We see DP as a core layer in the scientific stack. They are building the operating system for AI-driven discovery.
Investors are betting that DP Technology can replicate the success of Western counterparts like Schrödinger or Exscientia, but with a broader scope. While most competitors focus narrowly on small-molecule drugs, DP is attempting a full-stack approach that spans both biological and inorganic materials.
The Geopolitical Tightrope
Despite the technical promise, DP Technology faces a complex commercial landscape. As a Chinese deep-tech firm operating in biotech and advanced materials, it sits squarely at the intersection of innovation and export controls.
The company is deeply embedded in China’s domestic research ecosystem, contributing heavily to the DeepModeling open-source community. However, its ambition is global. To succeed, it must navigate data security regulations and dual-use technology concerns that often hamper cross-border collaboration in the current geopolitical climate.
The leadership seems aware that the era of “move fast and break things” does not apply when dealing with pharmaceutical regulation or material science.
In AI-for-science, you can’t fake it with demos. Your models either hold up in the lab, or they don’t.
With this fresh capital, the clock is ticking for DP Technology to prove that its algorithms can do more than just simulate results—they need to deliver viable drug candidates and battery materials that survive the harsh reality of physical manufacturing.
