AI adoption hits 72 percent in pharmaceutical R&D: Citi Research

Artificial intelligence has moved from experimental technology to scaled deployment within pharmaceutical research and development, with 72 percent of experts already scaling or fully scaling AI, Citi Research said.

In its latest proprietary survey of 50 U.S.-based executives responsible for R&D technology strategy, conducted in June 2026, Citi Research finds that AI adoption is no longer a question of “if” but of “how fast and how broadly.”

Notably, zero companies reported having no plans to adopt AI. The survey highlighted that a majority of firms anticipate double-digit budget increases over the next 12 months, and 26 percent have already spent over US$50 million (HK$390 million) on AI drug discovery in the past year. 

AI usage is still focused on early-stage discovery, where AI's strongest current capabilities lie in pattern recognition across molecular biology and chemistry. Protein structure prediction/structural biology leads adoption at 78 percent, followed by lead optimization and target identification. 70 percent of executives expect the greatest success rate improvements in target identification, while only 10 percent anticipate meaningful impact on Phase III trials – acknowledging that human biological complexity remains a significant challenge.

Engagement with external AI companies is high, with 84 percent having engaged with AI vendors, but trust and IP concerns remain a meaningful friction point in commercializing AI platforms, with many executives expressing caution about allowing partners to retrain models on proprietary data.

Looking three to five years ahead, Citi expects sustained investment but greater differentiation. Platforms offering superior algorithms, proprietary biological data, integrated wet-lab validation, and tangible evidence of faster candidate nomination or clinical proof of concept are likely to pull ahead.

Separately, analysis of Insilico Medicine’s Rentosertib provides the first direct clinical biomarker evidence of potential biological-age reversal across six independently developed aging clocks. While exploratory and based on a limited dataset, the findings reinforce the differentiation of Insilico’s aging-biology-led discovery platform and could expand scientific and partnering optionality for the asset. Citi maintains Insilico as its top pick in the AI drug-discovery subsector.
 

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