Kevin Weil, former head of OpenAI’s science initiatives, launched Warp Sciences, an AI startup that aims to accelerate drug discovery by combining AI, robotics, and laboratory experimentation. The company has raised $215 million in Series A funding at a reported post-money valuation of $865 million.
Launch reflects growing investor interest in applying AI beyond software and chatbots to scientific research and the development of new medicines.
The funding round was led by Lightspeed Venture Partners, with participation from IVP, Premji Invest and Lux Capital, among other investors.
Bringing AI from Digital Models into Physical Laboratories
Warp Sciences is focused on a challenging stage of drug development: producing and testing variations of promising molecules to improve their effectiveness and reduce potential side effects.
After researchers identify a promising compound, scientists may need to synthesise and evaluate hundreds or thousands of related molecules before selecting a suitable candidate for further development. This process can require substantial time, specialised expertise and laboratory resources.
Warp aims to make this work faster by integrating AI models with robotic laboratory systems. The company is establishing a US-based preclinical contract research organisation (CRO) to conduct experiments and produce molecules for biotechnology and pharmaceutical customers.
Its initial focus is lead optimisation in small-molecule drug discovery, a stage where researchers refine promising compounds before progressing towards potential drug candidates.
Robotics and Experimental Data at the Centre
Warp Sciences plans to combine experienced chemists with increasingly automated \
Initially, scientists will carry out experiments and guide the development of the company’s systems. Cameras and sensor-equipped gloves can capture aspects of laboratory work, helping generate real-world experimental data to train AI models and improve automation.
Robotic systems could eventually perform selected laboratory tasks continuously, allowing more experiments to be conducted and evaluated.
The underlying approach is based on a feedback loop: AI helps design experiments, laboratory systems execute them, and the resulting observations inform subsequent experiments.
This is particularly important in chemistry and biology, where theoretical predictions must be tested through physical experiments. AI-generated hypotheses alone cannot establish whether a molecule will perform as intended.
A Leadership Team Combining AI and Drug Research
Warp Sciences brings together leaders from artificial intelligence, data science and biotechnology.
Kevin Weil, the company’s co-founder and CEO, previously served as OpenAI’s chief product officer before leading its OpenAI for Science initiative.
Other co-founders include Manohar Paluri, a former Meta AI leader who serves as chief technology officer; DJ Patil, the White House’s first chief data scientist, who serves as chief operating officer; and Solomon Reisberg, a former Terremoto Biosciences executive with experience in medicinal chemistry.
The combination of AI engineering, laboratory expertise and scientific research is central to the company’s strategy of developing systems around the practical needs of drug researchers.
Why Warp Is Building in the United States
Pharmaceutical companies have historically outsourced significant amounts of molecule synthesis and preclinical research to specialist contract research organisations, including providers in China.
Warp aims to bring more of this work into the United States by building domestic laboratory capacity supported by AI and robotics.
The strategy comes amid concerns about supply-chain resilience, protection of intellectual property and geopolitical risks affecting pharmaceutical research partnerships.
By operating as a research services provider, Warp intends to make and test molecules selected by its customers rather than develop its own medicines. Pharmaceutical and biotechnology clients will retain control over their drug programmes.
Investors Bet on AI’s Potential in Drug Discovery
Warp’s $215 million funding round signals investor confidence in the potential to apply AI to scientific workflows that remain labour-intensive and dependent on physical experimentation.
However, the company’s funding and valuation reflect expectations for its technology rather than demonstrated improvements in drug development. Its long-term impact will depend on whether its systems can deliver reliable experimental results, improve productivity and operate economically at scale.
The wider AI drug discovery sector is attracting substantial investment as companies explore ways to improve molecular design, laboratory automation and research workflows.
The Next Frontier for AI and Scientific Research
Warp Sciences represents a shift towards AI systems that interact directly with the physical world. Rather than relying exclusively on digital information, its approach aims to connect computational models with real experiments, creating new data that can help improve future research.
If successful, this model could help pharmaceutical researchers evaluate more molecular candidates and use laboratory resources more efficiently. Yet drug discovery remains a complex process, and faster laboratory experimentation does not automatically translate into faster clinical trials or approved treatments.
For now, Warp’s launch highlights a growing ambition within the AI industry: to use intelligent systems and robotics not just to process information, but to support the experimental work that drives scientific progress.
