TECHBio-Techne Corporation

“we continue to see AI increasingly influence both how we operate internally and how our customers approach drug discovery. Internally, we are leveraging AI to design novel and patentable proteins with enhanced properties, including improved heat stability, bioactivity and solubility relative to the naturally occurring proteins.”

“a recently published collaboration between Providence Health and Microsoft on the GigaTIME AI framework used data sets generated on the Bio-Techne Spatial Biology platform, COMET, to convert traditional H&E pathology images into virtual 3-dimensional tissue representations.”

“From a customer perspective, AI adoption is accelerating the earliest stages of drug discovery, particularly target discovery, which is expected to expand the number of viable programs and improve probabilities of success. The effectiveness of these models depends heavily on the generation of high-quality biological data, which is an area where Bio-Techne is extremely well positioned.”

“AI also acts as a downstream demand driver for RUO reagent and assay portfolios. Every AI-enabled insight ultimately requires biological validation, which will fuel demand for highly specific antibodies functional assays and complex recombinant proteins in mechanism of action studies, biomarker validation and preclinical workflows.”

“As you are aware, AI tools are only as effective as the data that informs the model. Our models are trained on 5 decades of proprietary data, creating a meaningful competitive moat. And in parallel, we are deploying AI throughout the organization to improve productivity and customer engagement.”

“It also enhances the visibility of our solutions across digital and AI-driven platforms, making it easier for customers to identify and deploy the right tools within their workflows.”

“We view the growing demand for content-rich biological data sets as a durable tailwind for both our spatial biology and our proteomic analysis platforms.”