Artificial intelligence-driven drug discovery companies are facing a higher bar for investor interest, according to experts speaking on Mar. 17. While AI platforms promise faster and cheaper drug development, investors say that success will depend on strong pipelines, proprietary data, and clinical results.
Akshay Rai, principal of Healthcare & Biotech Investments at Premji Invest, said that native AI-driven discovery is attractive because it can shorten early research cycles. However, he said these platforms must also deliver biological insights and real drugs to create lasting value. "Although there is palpable excitement in the healthcare ecosystem, it is gated by disciplined milestones, deal structures and sophisticated investors’ proof-point expectations," Rai said.
Rai added that new AI-biotech companies must demonstrate both technological application and platform traction. He said that iterative learning, predictive power validated by experiments, and credible biological translation are crucial for attracting investment.
Anna Marie Detert, principal at Gloucester Ventures, noted that the rise of Recursion Pharmaceuticals led to many similar AI platform companies claiming they could target previously undruggable diseases more quickly and cheaply. However, she said many struggle to produce outcomes recognized by pharmaceutical partners. "Unless these platforms can generate assets or tie into a pipeline, they’re not going anywhere," Detert said.
One company highlighted as a success story is Enveda. In September 2025 it raised $150 million in a Series D round led by Premji Invest—bringing its total funding to $517 million and giving it a $1 billion valuation. Enveda’s platform identifies molecules from plants and predicts which might become medicines. Its lead candidate ENV-294 has completed Phase Ib trials for atopic dermatitis and started Phase IIa trials for both atopic dermatitis and asthma.
Enveda CEO Viswa Colluru agreed with Rai that claims about AI platforms must be supported by differentiated clinical data in competitive disease areas—not just internal metrics or rare conditions. Both Colluru and Rai also warned founders against focusing too much on model sophistication while neglecting proprietary data or underestimating translational challenges.
René Bastón of Covenant Venture Capital said too many startups focus on technology rather than solving specific problems: “When I evaluate a startup, I start with three questions: What specific problem are you solving? Can you show me clean metrics that matter? And can you actually get paid for it in the messy reality of healthcare?”
The experts concluded that winning companies have focused use cases, clear business models, and realistic paths to deployment rather than flashy technology alone.
