Some vendors are starting to tackle this challenge. Belcher points to solutions like Cytiva’s Image Integrity Checker, for instance, which uses secure hash algorithms—the same technology used in blockchain—to detect whether scientific images have been tampered with. “We’re starting to see a lot of interest from publishing houses that want to adopt this as standard because it’s a quick way to ensure that what gets published in the literature is genuine,” he adds.
Autonomous labs could accelerate breakthroughs
Belcher describes the future state of drug discovery as fully autonomous labs that run with minimal human intervention. Foundational to this vision is consistency in data and infrastructure.
These AI-driven dark labs, or labs-in-the-loop, operate around the clock. They cycle through prediction, testing, and optimization, and then feed results back into AI models to guide the next round of experiments. This can improve the success rates of drug candidates entering clinical trials, says Belcher. Better starting points, combined with more rounds of optimization, should result in better candidates with fewer liabilities reaching the clinic.
But automating a lab depends heavily on integration. That means interoperable systems, highly structured and comprehensive datasets, and information flowing easily in and out. Most labs aren’t there yet. “Today, a lot of the instruments in labs are standalone,” Belcher notes. “You can have the best technology in the world, but if it’s a closed ecosystem—if the user can’t get the data out—it doesn’t do any good.”
An integrated infrastructure can enable labs to generate FAIR (findable, accessible, interoperable, and reusable) data at scale. This would not only inform individual lab reports, but could also train subsequent generations of AI models, effectively closing the loop between the computational, AI-driven dry lab and the physical wet lab.
“Our goal is to help scientists and researchers accelerate their breakthroughs and make that future state of autonomous labs a real possibility,” says Belcher. “We want to help them generate reliable data, simplify workflows in discovery, and hopefully enable what they’re working on to become tomorrow’s life-changing therapies, faster and with greater confidence.”
On costs and what comes next
AI-driven drug discovery is still in its early days. Notably, no drug discovered primarily through AI-driven design has yet received full FDA approval—although Belcher expects that to change in the next two to three years.
How big of an impact could AI eventually have on drug discovery? “The holy grail would be full in silico prediction of efficacy and toxicity, eliminating the need for the vast majority of physical wet lab work,” says Belcher. But there are many barriers to this beyond the maturity of the models, including regulatory hurdles and cost challenges.
