Deep Origin's computational platform helped identify a potent compound for treating diffuse large B cell lymphoma, according to research published in Cell. The study was led by Stanford University and MD Anderson Cancer Center researchers. Deep Origin used proprietary docking software and molecular dynamics simulations to assess 17 compounds. The team ranked them by energetic cost when forming a ternary complex. The compound requiring the least energy killed lymphoma cells at sub-nanomolar concentrations. Laboratory tests validated the computational results. In mouse models, the selected compound achieved complete or near-complete tumour clearance. In immunised mice, it depleted germinal centre B cells without causing organ toxicity. The research focused on KAT-TCIPs, molecular glues that link lysine acetyltransferase enzymes to the cancer driver BCL6, activating cell-death programmes.

This startup thinks humans in clinical trials could become a 'formality' Deep Origin is attempting to create "virtual humans" that could one day reduce late-stage safety failures and the need for animal testing. Published July 16, 2026 By Alexandra Pecci Welcome to Biotech Spotlight, a series featuring companies creating breakthrough technologies and products. Today, PharmaVOICE is looking at Deep Origin, which combines physics with AI to optimize drug discovery. In focus with: Natalie Ma, chief business officer and co-founder at Deep Origin Deep Origin's vision: What if "virtual humans" and in silico trials could become stand-ins for real humans in clinical trials? That's the long-term vision of Deep Origin, a computational drug discovery company that combines molecular physics with AI to create predictive models for clinical trials. Rather than failing faster, "the idea is to stop doing experiments that will fail," said Ma. "What we're after is the quality of the prediction itself. That's the predictivity crisis, a systematic gap between what works in preclinical models and what happens in the clinic," she said. In addition to advancing its drug discovery platform with a variety of partners and working to better predict drug safety, Deep Origin is also simulating human biological systems at the cellular and organ levels to eventually build a complete "virtual human." But it's a long, step-by-step journey to chase that larger goal. In the meantime, Deep Origin is tackling more bite-sized industry challenges. "We don't position our immediate work as 'replacing' clinical trials," she said. "Right now, it's about reducing late safety failures and animal use, and giving programs more confidence going into the clinic." Deep Origin received a $31.7 million contract from the Advanced Research Projects Agency for Health's Catalyst program to replace animal testing with an FDA-qualifiable in silico prediction platform that will test how a medicine moves through and affects the human body. Ma said it will bring its first organ models online later this year or early next year. "The goal is to produce more accurate, human-relevant safety predictions before a molecule advances, bringing organ-level models online, tracing predicted drug-induced liver injury or tox signals back to specific off-target interactions and using those insights to redesign or de-prioritize risky compounds," Ma said. Why it matters: Drug developers are "consummate gamblers," Ma said. They face a 90% clinical failure rate and still knowingly embark on a decade-plus, multimillion dollar odyssey to bring drugs to market. Drug developers are burdened by other limitations, too, from relying on animal testing to navigating ultra-rare disease states with so few patients that traditional clinical trials are tough to pull off. But Ma envisions a world where "we get predictive enough, you [can] run an in silico clinical trial, and clinical trials are a formality." "You can parameterize virtual humans for different genetic backgrounds and comorbidities, and over time that could inform in silico clinical trial concepts. In its final form, researchers will be able to add a small molecule structure and a dose to the virtual human and see the outcomes across organs," she said. "But that's a long regulatory horizon, gated by validation and dialogue with the FDA." Here, Ma expands on the company's vision. The interview has been lightly edited for brevity and style. PHARMAVOICE: How are your partners currently using the platform? NATALIE MA: PharmaVOICE work with partners in three ways: discovery partnerships that put its systems and scientists against a partner's hardest programs; direct SaaS access to the platform and co-development of Deep Origin-originated programs. In practice, partners use its platform across the preclinical pipeline, from prioritizing targets by efficacy and toxicity risk, designing and filtering molecules in hit discovery, to prioritizing leads in optimization. PharmaVOICE haven't done a rescue of a program after a toxicity signal or patient population segmentation, but PharmaVOICE think its models could be adapted to these as well. Because most collaborations are confidential, PharmaVOICE talk about the workflows and the impact rather than specific deal structures. A lot of the value comes from being able to increase success rate in the lab while still finding high-quality hits and then layering safety predictions on top, so what moves forward has a much better chance of working in humans, not just in animals.