Artificial intelligence is booming across biopharma, with deployed tech taking aim at the industry’s biggest challenges: the significant risk of failure, high costs and long development timelines.
And it’s paying off. Biotechs that have been employing AI from the start have a nearly 100% valuation premium—or almost double that of their non-AI peers, according to an emerging tech research report from PitchBook.
In 2024, AI-native biotechs raised money at a median valuation of $78 million compared to a median $40 million for the broader biopharma industry.
The gap underscores both the “frenzied AI investment environment and investor conviction in the ability of AI integration to drive greater R&D efficiency and improve clinical success,” according to the PitchBook report.
The variation isn’t just a one-off. When considering the median biopharma VC deal value, AI-native biotechs had a $21.8 million average over the last five years compared to a $13.7 million median for all biopharma VC deal values.
In just the last 12 months, VCs have put down $3.2 billion across 135 deals for AI-driven drug development. More than half—62.5%—of the deals related to AI-enabled pharma tools and services. The startups are appealing because of their promise to quicker profitability and business models that aren’t as asset-heavy.
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Most AI drug development is fueled by foundation models in biology—models that are trained on vast biological data sets and build off earlier machine learning applications in chemistry. These smaller models help with target identification and screening but have limited capabilities beyond that, according to PitchBook.
More recent advances in AI use large unlabeled data sets, resulting in better task generalization and more powerful prediction and de novo molecule design capabilities. The newer innovations have sparked a new wave of AI-native startups, companies that are some of the most well-funded biotechs.
The next major step for foundation models, according to PitchBook, is combining biological data to build comprehensive simulations, further moving drug discovery in silico.
While the challenges inherent to drug discovery have turned general investors away from early-stage companies in recent years, AI offers a possible solution to these obstacles by de-risking efforts while also reducing costs.
“The technology’s ability to transform research & development and increase efficiency in a traditionally inefficient sector may be crucial to fostering the return of early-stage deal activity and reigniting VC interest in the sector,” PitchBook writes.