San Francisco-based Warp Sciences has emerged from stealth with $215 million in Series A funding to build AI-powered robotic chemistry laboratories intended to accelerate pharmaceutical research.
The financing was led by Lightspeed Venture Partners, with participation from IVP, Mayfield, Lux Capital, Premji Invest, Menlo Ventures, Nvidia, Uncork Capital and other investors.
Bloomberg reported that the investment values the company at approximately $865 million post-money.
The company was founded by four executives with backgrounds spanning AI research, pharmaceuticals and technology infrastructure.
Kevin Weil previously served as chief product officer at OpenAI before leading the company's AI-for-science efforts. He also held senior product positions at Instagram and Twitter.
DJ Patil served as the United States' first chief data scientist under President Barack Obama and later held leadership positions at healthcare technology company Devoted Health.
Manohar Paluri was a founding member of Meta's Fundamental AI Research organization, while Solomon Reisberg brings drug discovery expertise from the biotechnology industry.
Warp is targeting a specific bottleneck in pharmaceutical research: physically making and testing the molecules proposed by scientists and computational models.
AI is increasingly capable of suggesting molecular structures that may interact with biological targets. But a promising molecule generated by software still has to be synthesized, purified and tested in a real laboratory.
That physical experimentation requires trained chemists, specialized equipment and significant time.
Pharmaceutical companies frequently outsource this work to contract research organizations, including providers in China, where substantial chemistry manufacturing and research capacity has developed.
Warp wants to create a competitive alternative in the United States by combining human chemists with robotic automation and machine learning.
Its initial business will focus on lead optimization analog synthesis.
During lead optimization, researchers produce and test variations of a promising molecule to understand how chemical changes affect properties such as biological activity, toxicity and stability.
Warp plans to provide those services through automated American laboratories, beginning with workflows that scientists already purchase from established research providers.
Human chemists will initially guide the experiments while helping train robotic systems to perform laboratory procedures.
The company intends to use cameras, sensors and other instrumentation to collect detailed information about those procedures and their outcomes.
That information would support machine-learning models capable of improving subsequent experiments.
The long-term ambition is a continuously improving research system in which physical experiments produce data that makes future automation and scientific decision-making more effective.
Unlike AI drug discovery companies that primarily develop computational models or their own drug candidates, Warp is starting as a service provider.
Its customers would be pharmaceutical and biotechnology companies seeking chemistry capacity without needing to build additional laboratories internally.
That model gives Warp a potential revenue stream before fully autonomous laboratories become technically feasible.
It also creates a potential data advantage. Every experiment, including unsuccessful reactions, could generate information valuable for improving the company's automation systems, subject to customer agreements and intellectual property protections.
The geographic dimension is significant as well. Warp wants to bring more chemistry research capacity back to the United States while competing with established overseas providers on speed, quality and cost.
However, building automated laboratories is capital-intensive.
Robotic manipulation, chemical handling, experiment reproducibility and equipment utilization remain difficult engineering problems. Chemistry workflows can also vary substantially between compounds.
Warp must demonstrate that its automation reduces the cost and turnaround time of laboratory services while maintaining the quality required by pharmaceutical researchers.
The company has not publicly disclosed commercial revenue, customer counts or operating performance metrics.
Its next milestone will be moving beyond laboratory development into repeatable customer projects that demonstrate measurable advantages over conventional contract research organizations.