Bytedance's AI-driven drug discovery venture, Anew Labs, has completed its first external funding round following its spin-off, raising approximately $290 million at a post-money valuation of $1.5 billion. The financing, reported by Reuters on September 16th citing insider sources, marks a significant step in the company's transition to independent operation. Leading the round are HSG (formerly Sequoia Capital China), IDG Capital, and Hillhouse Investment, with 5Y Capital acting as a co-lead. Other participants include Gaorong Ventures, Primavera Capital, Boyu Capital, along with strategic investors China Biopharmaceutical Group (SBP Group) and Shanghai Future Industry Fund.

Following the completion of this funding round, Bytedance will retain approximately 56% of Anew Labs, maintaining its controlling stake. Neither Bytedance nor the investment institutions have publicly confirmed the details of this financing. Insiders indicate that the decision to spin off the AI pharmaceutical business stems from the significant differences in industrial logic and management approaches between AI drug discovery and Bytedance's core internet operations, with the goal of supporting long-term development through a more independent organizational structure.

This development signals that the AI pharmaceutical unit has entered a phase of independent fundraising and corporate operation, following initial reports of the spin-off in June. Public records show that Bytedance began its foray into AI for Science early on, assembling an AI pharmaceutical team around 2021 under the leadership of Liu Kai, which brought together both AI algorithm specialists and drug development researchers. Media reports suggest that during the restructuring, internal teams responsible for protein structure prediction were integrated into the AI pharmaceutical system, with core teams, algorithm platforms, and existing drug pipelines moving into the new independent entity.

Anew Labs distinguishes itself not as a mere algorithm service provider to pharmaceutical companies, but as a biotech company with AI as its foundational technology. The company simultaneously develops AI underlying models, drug discovery platforms, and proprietary drug pipelines. According to its website, Anew Labs currently operates teams in Shanghai, San Francisco, and Singapore, where drug developers and AI researchers collaborate on model development and new drug creation. Its platform encompasses multiple capabilities, including protein and molecular structure prediction, molecular dynamics simulation, generative molecular design, antibody design, and drug discovery reasoning models.

Among its proprietary tools, AnewFold is used for predicting protein and molecular complex structures; AnewSampling employs AI to simulate dynamic conformations and thermodynamic distributions of protein-ligand systems; and AnewOmni integrates small molecules, peptides, and antibodies into a unified all-atom generative framework for designing various molecular binders. The company is also developing AnewDesign for antibody design and optimization, alongside AnewMind, a scientific reasoning model aimed at supporting drug discovery decisions.

Beyond its models, Anew Labs is actively applying AI capabilities to concrete drug pipelines. Its publicly disclosed R&D projects include targets such as IL-17AA/AF/FF and IL4R, along with two unspecified targets, covering preclinical stages from Hit ID through Hit-to-Lead, Lead Optimization, and IND Enabling. The most advanced program is an oral small molecule project targeting the IL-17 family. At the American Association of Immunologists (AAI) and FOCIS conferences in 2026, the company presented findings on small molecule inhibitors designed through generative AI that simultaneously inhibit IL-17AA, IL-17AF, and IL-17FF dimers, with experimental validation completed.

This project reflects Anew Labs' broader vision for AI in drug discovery: not merely enhancing the efficiency of traditional compound screening, but expanding the chemical space and target range accessible to drug design, particularly targeting protein-protein interactions that have traditionally been addressed by large-molecule biologics.