OpenAI researcher Miles Wang leaves to launch AI drug discovery startup valued at $2B
OpenAI researcher Miles Wang is departing to start a new company focused on AI models for drug discovery, with a valuation of $2 billion and plans to raise $200 million.
Miles Wang, an OpenAI researcher known for using AI to accelerate scientific and biological discovery, is leaving the ChatGPT maker to launch a startup focused on developing AI models for drug discovery, according to four people familiar with his plans. Several other OpenAI researchers are expected to join the new company.
Wang is in talks to raise approximately $200 million at a $2 billion valuation, two of the sources said. Venture firm Lightspeed is in discussions to lead the funding round. The talks are ongoing and may not result in a final deal, with details subject to change. Wang disputed the reported funding figures and description of the company but did not provide correct numbers or details. Lightspeed did not respond to a request for comment.
The funding discussions reflect investor interest in applying AI to life sciences breakthroughs. Chai Discovery, a two-year-old startup developing AI models to predict molecular interactions for identifying new drugs, announced on Tuesday it raised $400 million at a $3.8 billion valuation. Meanwhile, Google DeepMind spinout Isomorphic Labs, which also builds AI models for drug discovery, raised a $2.1 billion Series B in May.
According to a couple of sources, Wang's new startup may work on AI models to find new uses for existing drugs and possibly those that previously failed in trials. Repurposing FDA-approved drugs can lead to significantly faster time to revenue than developing new drugs from scratch, since these medicines have already been tested for safety.
Wang joined OpenAI in 2024 after dropping out of Harvard University, where he was pursuing a bachelor's degree in computer science. (In recent years, investors have become comfortable betting on young founders who haven't completed college.) At OpenAI, he co-authored research papers, including evaluating how AI models can automate and accelerate scientific discovery.
