Miles Wang, a researcher at OpenAI who contributed to projects on AI-driven scientific discovery, is leaving to launch a startup that will develop AI models for drug discovery. The company is in discussions to raise up to $200 million at a $2 billion valuation, with Lightspeed Ventures in talks to lead the round, TechCrunch reported, citing sources familiar with the matter. Wang denied some details related to the funding and valuation but did not provide corrected figures. The move comes amid a rush of investor capital into AI-powered biotech.
The startup plans to focus on identifying new therapeutic uses for drugs that are already approved or that failed earlier clinical trials. This strategy could reduce the time to generate revenue because regulators already have safety data on these compounds, avoiding the years of testing required for entirely new molecules.
Rising investment in AI-driven biotech
The funding talks follow several large rounds for companies at the intersection of artificial intelligence and biology. Chai Discovery recently raised $400 million at a $3.8 billion valuation, while Isomorphic Labs secured $2.1 billion. The funding activity underscores the expanding role of AI for Healthcare, particularly in drug development pipelines.
The startup's approach taps into a growing area of AI for Science & Research, where machine learning is used to generate hypotheses and repurpose existing knowledge. By focusing on known compounds, the company can bypass some of the early-stage risk that plagues traditional drug discovery.
Wang's background and team
Wang joined OpenAI in 2024 after leaving Harvard University before completing his computer science studies. At OpenAI, he worked on research into how AI can accelerate and facilitate scientific discoveries. Sources told TechCrunch that several of his OpenAI colleagues will join the new venture.
Why this matters for Science and Research professionals
The emergence of well-funded startups targeting drug repurposing signals a shift in how AI is applied to biology. Researchers with skills in computational biology, machine learning, and cheminformatics may find increasing demand as more companies build platforms that can screen existing drug libraries at scale. The model also points to faster paths from lab discovery to clinical impact, potentially changing the timelines that researchers and institutions plan around.
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