Miles Wang, a researcher at OpenAI whose work has focused on using AI to speed up scientific and biological discovery, is leaving the company to start a new AI drug discovery venture. According to TechCrunch, several other OpenAI researchers are expected to join him. Wang disputed the reported funding figures and how the company was described, though he did not provide the correct details.
Wang is in talks to raise around $200 million at a $2 billion valuation. Lightspeed is reportedly in discussions to lead the round, though talks are ongoing and nothing is final. Lightspeed did not respond to a request for comment.
Wang joined OpenAI in 2024 after dropping out of Harvard, where he was studying computer science. At OpenAI, he co-authored research on how AI models can automate and speed up scientific discovery. He is now looking to apply that work in one of the most capital-intensive industries around: pharmaceuticals.
The startup may focus on finding new uses for existing drugs, including some that previously failed in clinical trials. That approach, known as drug repurposing, has a real commercial advantage. Because FDA-approved drugs have already cleared safety testing, getting them to market for a new indication is significantly faster than developing a new drug from scratch. It is a smart angle for an AI-driven company looking to show results quickly.
The broader context here matters. Investor appetite for AI in life sciences is running hot right now. Two major deals have closed in quick succession:
- Chai Discovery, a two-year-old startup building AI models to predict molecular interactions and identify new drugs, raised $400 million at a $3.8 billion valuation. Its co-founder Josh Meier also came through OpenAI.
- Isomorphic Labs, a Google DeepMind spinout focused on AI-powered drug discovery, closed a $2.1 billion Series B in May.
Wang’s reported startup would enter a field that is attracting serious money and serious talent. The pattern of OpenAI researchers leaving to found companies is well established at this point. Ilya Sutskever, Andrej Karpathy, and others have all gone on to launch their own ventures after stints at the company. Wang follows that same path, though with a much more specific scientific focus than most of his predecessors.
What makes this moment interesting is the convergence happening in AI and biology. Large language models trained on protein sequences, molecular data, and clinical trial results are genuinely changing what’s possible in drug development. The question is no longer whether AI can contribute to this process. It is which teams and which approaches will produce real-world results. Wang’s background, working on AI systems designed to accelerate scientific discovery, puts him in a reasonable position to compete. Whether the funding round closes at the reported figures, and whether the company delivers, remains to be seen.
