Drug Developers Deploy In Silico Drug Safety Prediction Models From Deep Origin’s ARPA-H-Funded PREDICTS Consortium

ImmVue, Synko, Sanford Burnham Prebys, and SyzOnc are running Deep Origin’s ADMET predictions across millions of compounds to prioritize discovery programs

Partners will return their wet-lab results to extend the chemical space the models are trained on

The first virtual human organ models for assessing the safety mechanisms behind toxicity predictions are also in advanced development, with early access open to select partners

SOUTH SAN FRANCISCO, Calif., Sept. 24, 2026 (GLOBE NEWSWIRE) — Deep Origin and partners in the Pharmacological Research and Evaluation through Digital Integration and Clinical Trials Simulation (PREDICTS) consortium today announced that their new drug safety prediction models are now being implemented by four organizations in a pilot program. The in silico ADMET (absorption, distribution, metabolism, excretion, toxicity) models will be used to assess and filter millions of compounds to prioritize the drug candidates worth advancing.

PREDICTS is part of the Advanced Research Projects Agency for Health (ARPA-H) Computational ADME-Tox and Physiology Analysis for Safer Therapeutics (CATALYST) program, which is led by Program Manager Michael Patterson. The program aims to revolutionize preclinical drug safety prediction by developing human-based models that accurately estimate toxicity and safety profiles for drug candidates. If successful, CATALYST will enable safer and faster drug development, particularly for rare disease populations. Robust modeling will also capture more representative physiologies and help meet the targets of the U.S. Food and Drug Administration’s (FDA) Modernization Act.

The Deep Origin-led PREDICTS consortium was awarded an up to $31.7 million Other Transaction Agreement (OTA) from ARPA-H in 2025 to develop in silico models to more accurately predict drug safety and toxicity. Now, roughly one year since the start of the work, the first models are in the hands of drug developers.

ADMET-NOW: In silico platform of 77 predictor models for ADME and toxicity

Of the 77 machine learning-based models in development, 62 are now available to pilot participants. The models predict absorption, distribution, metabolism, excretion and toxicity – the ADMET properties that determine whether a molecule will survive preclinical development.

The four organizations now implementing these models span antivirals, antibacterials, oncology and immune-mediated disease.

Public ADMET datasets are small, with a few thousand molecules for most endpoints. When the ADMET safety models developed by the consortium are tested in partner laboratories, the experimental results will be shared back to assess performance and continue adding to the training data.

“These organizations have computational teams that have chosen to run our predictors because they see their potential,” said Natalie Ma, Ph.D., co-founder and chief business officer of Deep Origin. “Their interest in working with these models is evidence of the need and potential for improved in silico predictors of safety and toxicity. Because they send their lab results back, every program that uses the models will make the next one better.”

Togo foundational chemistry model enables model building across data-sparse scenarios

Every ADMET-NOW model is built on Togo, Deep Origin’s foundational chemistry model. Togo is trained across a wide range of molecular and protein-ligand tasks and produces descriptors that let a new property model be trained on fewer than 1,000 data points, which is the situation for many ADMET endpoints. To date, 62 property-specific models have been developed, with 89% outperforming the best-performing model identified in the literature, including genotoxicity, human ether-à-go-go-related gene (hERG) inhibition, and drug-induced liver injury (DILI).

Virtual humans for safety assessment and toxicology prediction as the next frontier

Beyond ADMET-NOW, Deep Origin is developing Virtual Human Avatars of Toxicology (VHAT) to increase the human-relevance of safety predictions in preclinical development. Deep Origin distinguishes the virtual human from ‘digital twins’, which only infer outcomes from statistical averages of patient parameters. The virtual human is fine tuned to simulate special populations and biological situations, including physiological traits, comorbidities, mutations and other specific transcriptomic, proteomic or cellular states.

The virtual human that Deep Origin is developing uses organ models to simulate the physical and chemical events inside a body to predict drug safety by tracking PK and permeability (where a drug travels and at what concentration), liver metabolism including the concentration and distribution of metabolites, the interaction of the drug, its metabolites and proteins, and the impacts of these actions within affected cells (molecular initiating events). The organ models are connected rather than running in isolation, because toxicity often manifests as a result of a chain of events.

Liver, kidney, intestine, blood coagulation and bone marrow organ models that make up the virtual human are in advanced development.

“As far as we know, no one has built a mechanistic model of a whole human body for toxicology, and the reason is a systems problem as much as it is a modeling problem,” said Michael Antonov, co-founder and chief executive officer of Deep Origin. “Each organ model must effectively talk to each other to measure evolving downstream effects. The Deep Origin team is making great progress building a virtual human with ARPA-H’s support, and we look forward to soon testing the first molecules in the comprehensive virtual human model.”

For more information about the PREDICTS consortium work, contact Deep Origin’s partnering team here.

About the PREDICTS Consortium

PREDICTS (Pharmacological Research and Evaluation through Digital Integration and Clinical Trial Simulation) aims to integrate high-throughput biological data with advanced AI models to better predict toxicity and human safety earlier in drug development.

The PREDICTS consortium is led by Deep Origin and includes Ginkgo Bioworks, ImmVue Therapeutics, MIDO LLC, Netrias, Sanford Burnham Prebys and Tessel Biosciences. Additional specialists span computational biology, toxicology and drug development.

This research was funded, in part, by the Advanced Research Projects Agency for Health (ARPA-H) under Agreement No. 1AY2AX000145. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.

About Deep Origin

Deep Origin is building computational discovery systems that model life, to close the gap between preclinical predictions and clinical outcomes. Co-founded by Michael Antonov, co-founder of Oculus, and Garegin Papoian, Ph.D., former Monroe Martin Professor of biochemistry at the University of Maryland, Deep Origin runs drug discovery programs — its own and its partners’ — through hybrid AI-mechanistic models that span biological scales from quantum to cellular to human body scale. The company engages through discovery partnerships and SaaS platform access. Deep Origin is backed by more than $50 million in capital and more than $32 million in non-dilutive funding, including an ARPA-H CATALYST award to build in silico models that can reduce reliance on animal testing in preclinical drug development. For more information, visit https://deeporigin.com/.

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