Key insights
- Artificial intelligence is accelerating drug discovery and increasing demand for experimental data in pharmaceutical preclinical research, according to Bernstein. This trend is expected to benefit suppliers of laboratory equipment, particularly those providing high-content imaging and detailed cellular data generation. While increased demand for sequencing volumes could benefit Illumina, falling prices may limit revenue growth. Overall reagent consumption is also expected to rise due to the need for physical testing of AI-designed molecules.
Investing.com -- Artificial intelligence is reshaping pharmaceutical research by accelerating drug discovery and increasing demand for experimental data, potentially benefiting laboratory equipment suppliers, according to Bernstein.
In an October 16 report, Bernstein analysts highlighted findings from a webinar with Tom Fleming, co-founder and former CEO of automated laboratory services provider Arctoris.
Fleming said demand for AI training datasets has expanded beyond venture-backed biotechnology companies to major pharmaceutical groups, which now account for most of Arctoris’s data-generation business, excluding certain partnerships.
In some cases, demand for experimental data has increased tenfold or even 100-fold. However, declining costs per data point could offset the financial benefits of higher volumes.
Laboratory instruments capable of producing complex, high-value data at scale are expected to benefit most.
Fleming identified Revvity’s (NYSE) Opera Phenix high-content imaging platform as a potential beneficiary, noting that Arctoris purchased additional units to meet growing demand.
He also highlighted opportunities for 10x Genomics (NASDAQ), whose technologies generate detailed cellular data.
Meanwhile, Illumina (NASDAQ) could benefit from higher sequencing volumes, although falling prices may limit revenue growth.
AI is also expected to increase overall laboratory reagent consumption rather than reduce it.
Although predictive models could eliminate some routine experiments, the growing number of AI-designed molecules and proteins requires extensive physical testing and validation.
Fleming said the greatest long-term value would come from improving clinical trial success rates rather than simply increasing the number of drug candidates.
However, demonstrating meaningful improvements could take years because clinical development remains lengthy and failure rates are high.
The findings suggest AI adoption could reshape spending across the life sciences industry, favouring suppliers of advanced experimental technologies while creating pricing pressure for more commoditised data-generation services.
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