Drug development is a long, risky and wildly expensive game. Nine in ten drug candidates fail in clinical trials, often after years of preclinical work and hundreds of millions in investment. Now, a new generation of AI-native biotechs is trying to change the math, and they’re beginning to show results.
Startups like Recursion, Inceptive, Cradle and Deep Genomics are embedding AI across the pipeline, from target discovery and molecule design to trial optimization.
“The TechBio approach to drug discovery begins with data, and we have a massive data advantage,” said Ben Mabey, chief technology officer of Recursion. “We’re developing drugs at more than twice the speed to lead candidate, and producing molecules with 10 times the efficiency of traditional pharma.”
Recursion’s platform, called RecursionOS, integrates over 65 petabytes of proprietary biological data and uses foundation models like Phenom-2 and MolPhenix to simulate how drug compounds will behave in cells before anything is synthesized in a lab.
Even an early version of RecursionOS progressed from biological insight to viable drug candidate in just 18 months while targeting a key cancer pathway — less than half the industry average. The program, now called REC-1245, has advanced to Phase 1/2 trials in human patients.
Revolutionizing RNA therapeutics
Deep Genomics, meanwhile, is focusing on RNA therapeutics. The company's platform uses three proprietary foundation models that can predict outcomes directly from genetic sequence data across multiple biological layers. "This lets us identify disease-driving mutations and design RNA therapies with unprecedented precision," said Brendan Frey, founder and chief innovation officer of Deep Genomics.
Frey argued that foundation models represent a break from the past. Traditional AI approaches targeted narrow slices of the drug discovery pipeline, like protein folding or small-molecule interactions, whereas Deep Genomics’ models are trained on trillions of datapoints across DNA, RNA, proteins and systems biology. “With a single foundation model platform, we can intelligently discover new targets, mechanisms and molecules — including those we didn’t know to look for,” Frey said.
AI-driven drug development hinges on more than clever algorithms. You need scale in compute, data and lab throughput.
Recursion runs up to 2.2 million experiments per week in its automated labs and trains models on terabytes of microscopy and RNA transcript data using BioHive-2, its in-house supercomputer built with NVIDIA. This powers tools like Boltz-2, developed with MIT, which predicts both protein structure and binding affinity with accuracy approaching physics-based simulations — but 1,000 times faster.
Deep Genomics has built a similarly integrated setup, combining high-performance compute with pipelines that connect proprietary and public datasets to its models. “Our infrastructure allows seamless collaboration between machine learning, computational biology and experimental biology teams,” Frey said. “That enables rapid iteration from model development to in vitro validation and, ultimately, therapeutic design.”
AI-native biotechs are reshaping clinical development
AI isn’t just speeding up discovery, it’s also reshaping clinical development. Startups are using machine learning to select better trial populations, design adaptive protocols and model disease progression in silico. That can cut costs, improve statistical power and reduce unnecessary patient exposure.
To help drive adoption, Recursion recently joined the Council for Responsible Use of AI in Clinical Trials, alongside Sanofi and Velocity Clinical Research, to establish industry standards and define meaningful metrics for AI’s value across the trial lifecycle.
Deep Genomics, for its part, works with pharmaceutical partners to integrate its foundation models into early R&D workflows, tailoring predictions to specific disease areas. Frey said the company validates its AI-designed candidates through experimental assays and in vivo studies and has made some tools, like the REPRESS model, available for academic use.
The ecosystem is maturing quickly. Cloud platforms can now handle AI workloads with large-scale biological data. Open-source tools and public omics datasets are proliferating. But AI isn’t a panacea. Regulatory frameworks still lag behind the pace of innovation, and models are only as good as the data they’re trained on. Biology is complex, noisy and context-specific — even the best simulations can miss real-world effects.
Despite these challenges, AI is giving drug developers a new kind of leverage. With the right infrastructure, data and scientific grounding, it’s not just possible to speed up R&D — it’s starting to become the default.
“We’re seeing our AI-driven predictions and AI-designed drugs already having a real impact on patients in trials,” Mabey said. “That’s enormously motivating. Every version of our platform is smarter than the last, and the quest for a virtual cell, even a virtual patient, feels like a real possibility.”
Frey was equally bullish but noted the urgency: “What keeps us up at night is knowing how critical this work is. Patients can’t wait. That’s why we’re growing our team rapidly — to bring these AI-designed medicines to the people who need them most.”
