Warp Sciences Secures $215M for Robotic Drug Discovery Labs

Warp Sciences launches with $215 million in funding

Kevin Weil, formerly OpenAI’s science lead, has launched Warp Sciences, an AI startup focused on automating wet-lab work in drug discovery. Weil said the company raised $215 million at an $865 million valuation.

Lightspeed led the round, with IVP, Premji Invest and Lux Capital participating. Warp Sciences plans to train robots to make and test promising compounds identified by researchers, targeting a part of drug development that has traditionally depended on intensive manual laboratory work.

Robots trained by working chemists

The company’s model relies on human chemists to teach robots laboratory tasks. Chemists will wear cameras and sensor gloves while they work, creating training data for robotic systems intended to reproduce those activities.

Warp Sciences says automating wet-lab work can accelerate drug discovery by enabling robots to operate around the clock. Its focus is not only on identifying potential compounds, but on the practical synthesis and testing work required to assess them in a laboratory setting.

A supply-chain case for laboratory automation

Biotechnology companies have long outsourced the labor-intensive synthesis of thousands of molecular variations to contractors in China, including WuXi AppTec. Weil’s stated objective is to reshore that work and reduce geopolitical supply-chain risk across the drug-development process.

The announcement positions robotic automation as an operational response to two constraints at once: the scale of experimental work needed to evaluate compounds and reliance on external manufacturing and laboratory partners. The company has not detailed specific robot models, laboratory throughput targets or timelines for deployment.

What the launch means for life-sciences operations

Warp Sciences enters drug discovery with significant early financing and a workflow built around capturing expert laboratory practice. The funding also illustrates investor interest in applying AI and robotics to physical scientific processes rather than software-only research tools.

For businesses evaluating laboratory automation, the practical implication is to identify repeatable, labor-intensive wet-lab tasks where expert demonstrations, dependable robotic execution and continuous operation could support internal research capacity while reducing dependence on distant contractors.