Miles Wang, an OpenAI researcher known for applying artificial intelligence to scientific and biological problems, is preparing to leave the company and start a new venture focused on AI-driven drug discovery. According to four people familiar with his plans, Wang is in discussions to raise approximately $200 million at a $2 billion valuation. Several other OpenAI researchers are expected to join the new company.
The funding talks are being led by Lightspeed, two of the sources said, though the deal is still ongoing and terms could change. Wang disputed the reported funding figures and company description but did not provide corrected details. Lightspeed did not respond to a request for comment.
A New Venture in AI Drug Discovery
The potential investment signals strong investor appetite for applying AI to life sciences. Earlier this week, Chai Discovery, a two year old startup that builds AI models to predict molecular interactions for drug identification, announced a $400 million raise at a $3.8 billion valuation. Chai Discovery co founder Josh Meier also previously worked at OpenAI. Meanwhile, Isomorphic Labs, a Google DeepMind spinout developing AI models for drug discovery, secured a $2.1 billion Series B in May.
Wang’s startup may focus on repurposing existing drugs or finding new uses for medicines that failed in clinical trials, according to a couple of the sources. This approach can lead to revenue much faster than developing entirely new drugs, because those compounds have already passed safety testing.
From Harvard Dropout to OpenAI Researcher
Wang joined OpenAI in 2024 after dropping out of Harvard University, where he was pursuing a bachelor’s degree in computer science. Investors have recently become more comfortable betting on young founders who did not complete college. At OpenAI, Wang co authored research papers that explored how AI models can automate and accelerate scientific discovery.
The move comes as the pharmaceutical and biotech industries increasingly look to AI to shorten the drug development pipeline. Traditional drug discovery can take more than a decade and cost billions of dollars. AI models that can predict how molecules interact or identify promising candidates could dramatically compress that timeline.
As AI continues to reshape industries, the race to apply it to biology is accelerating. For more on the broader timeline of AI progress, see our analysis of AGI timelines.
