Former OpenAI science lead raises $215M to automate drug-discovery labs

Kevin Weil, who previously led scientific efforts at OpenAI, announced the launch of Warp Sciences, an artificial-intelligence startup that aims to automate the wet-lab portion of drug discovery. By training robots to synthesize and test candidate molecules, the company seeks to speed up early-stage research while reducing reliance on overseas contract manufacturers.

The venture closed a $215 million financing round that values Warp Sciences at $865 million. The round was led by Lightspeed and included participation from IVP, Mayfield, Lux Capital, Premji Invest, and Menlo Ventures. Weil disclosed the funding on X, positioning the capital as a foundation for building a robotic platform that can operate continuously, day and night.

Warp Sciences’ approach pairs human chemists with advanced sensing equipment. Researchers wear cameras and sensor-embedded gloves to demonstrate laboratory techniques, allowing the system to capture detailed motion and force data. This information is then used to program robotic arms to perform tasks such as compound synthesis, purification, and assay preparation. The company’s stated objective is to bring the labor-intensive steps of molecule creation, traditionally outsourced to large Chinese contract research organizations like WuXi AppTec, back onto U.S. soil. By reshoring these activities, Warp Sciences hopes to insulate drug-development pipelines from geopolitical supply-chain disruptions.

For founders and operators in the biotech sector, the emergence of a robot-driven wet-lab model could reshape cost structures and timelines. Automated synthesis promises higher throughput and consistency, potentially shortening the gap between target identification and preclinical testing. Investors may view the model as a hedge against the volatility of global manufacturing networks, while also opening a new market for AI-enabled laboratory equipment. However, the transition will require integration with existing research workflows and validation of robotic output against traditional methods.

Looking ahead, Warp Sciences plans to expand its robot fleet and refine the training interface to accommodate a broader range of chemical reactions. Success will depend on the company’s ability to demonstrate that automated processes can match or exceed the reliability of human-performed synthesis at scale. If the technology delivers, it could set a new standard for how early-stage drug discovery is conducted, offering founders a more controllable and resilient path from molecule design to candidate selection.