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Director, Computational DMPK (Drug Metabolism & Pharmacokinetics)
Deep Origin
Remote, Canada
- Full-time
- Remote
Familiarity with medicinal chemistry and how metabolism liabilities are addressed during lead optimization.
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Deep Origin

Director, Computational DMPK (Drug Metabolism & Pharmacokinetics)

Remote, Canada

Remote

Full-time

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Job details

Job type: Full-time
Workplace: Remote
Remote eligibility: Canada listed. Check the posting for province restrictions.
Posting language: English
Posted: Nov 13, 2026

Original job description

Deep Origin is a biotechnology company building an operating system for science that transforms how life science research is conducted. Our AI-powered platforms simulate biology, streamline R&D workflows, and enable scientists to model complex biological systems at scale.

Deep Origin leads the PREDICTS consortium, funded by ARPA-H under the CATALYST program, to build in silico models that predict human drug safety and toxicity. As part of this work we are scaling our capabilities in whole-human modeling and predictive toxicology.

About the role

Deep Origin is building VHAT, the Virtual Human Avatars of Toxicology. VHAT takes a molecule, a dose and a patient population and predicts organ-level human toxicity before a compound is synthesized or dosed. Metabolism sits at the heart of that chain. What a drug becomes, which enzymes act on it and how fast they act determine what each organ is exposed to, and every predicted metabolite is screened for its own off-target and organ liabilities. The Metabolism group builds four connected models: drug-metabolizing enzyme, site of metabolism, metabolite structure, and enzyme kinetics. As the group’s leader, you will own those models, mentor the team that builds them, and drive integration of their predicted parameters into the physiologically based pharmacokinetic (PBPK) model that governs exposure across the VHAT.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

A PhD in computational chemistry, cheminformatics, drug metabolism, computational biology, pharmacology or a related field.
Deep, hands-on expertise in drug metabolism. You understand CYP and non-CYP enzymes, sites and products of metabolism, enzyme kinetics and clearance, and you can judge whether a prediction is chemically and biologically sound.
A strong track record in computational modeling, either as a model builder or as an expert end-user. This includes machine learning for molecular property or reactivity prediction, and mechanistic or kinetic modeling, including ODE-based models, parameter estimation, and Bayesian or frequentist inference. You work fluently in Python.
Demonstrated rigor in model validation. You have designed appropriate data splits, defined applicability domains, and run external or blinded benchmarks, and you can explain the limits of a model as clearly as its strengths.
Experience leading a scientific team and delivering against milestones and deadlines.
Clear written and spoken communication with scientists, partners and funders.

Strongly preferred

This role owns the link between metabolism and whole-body exposure, so PBPK experience will weigh heavily in our decision.

Hands-on experience building or parameterizing PBPK models in Simcyp, GastroPlus, PK-Sim, SimBiology or a custom ODE framework.
Experience translating in vitro or predicted intrinsic clearance into in vivo hepatic clearance and human PK, including the use of IVIVE scaling factors and an understanding of where they tend to fail.
Experience using PBPK to assess metabolic drug-drug interactions, such as CYP inhibition and induction.
Experience combining machine learning predictions with mechanistic PK models, and carrying their uncertainty into downstream predictions.

Nice to have

Experience with metabolomics or LC-MS metabolite identification data.
Experience running data curation at scale, including annotation standards and quality control.
Experience on government-funded research programs with milestone-based reporting.
Exposure to regulatory science, such as FDA guidance on new approach methodologies, model-informed drug development or the ISTAND program.
Familiarity with medicinal chemistry and how metabolism liabilities are addressed during lead optimization.

Responsibilities

Lead a team of computational scientists and data curators across sites. Set scientific direction, priorities and review standards for each model, and develop the people who build them.
Own the roadmap and release gates for the four metabolism models. Decide when a model is ready for the VHAT platform, for use as a standalone product, and for external review.
Own the metabolism-to-PBPK integration. Define which parameters pass to the PBPK model, how predicted or in vitro clearance is scaled to in vivo hepatic clearance, and how the combined model is checked against clinical PK and drug-drug interaction data. Work with PBPK modelers so that parent and metabolite exposure flow correctly into the organ models.
Design validation experiments that hold up to outside scrutiny. This includes leakage-aware data splits, applicability-domain and confidence reporting, predictions locked before new experimental data arrive, and external, blinded and independent benchmarks.
Set the data strategy for a field where high-quality labels are scarce. Plan curation targets and throughput, oversee AI-assisted literature mining with expert quality control, support data licensing, and design wet-lab experiments with partners (recombinant enzymes, human liver microsomes, hepatocytes), including active-learning compound selection.
Extend coverage beyond CYP Phase I metabolism to Phase II enzymes (UGT, SULT), FMO, AOX and multistep metabolic networks, and scope how enzyme polymorphism and population variability enter the models.
Manage external relationships with consortium partners, contract research organizations, academic reviewers and independent validation teams.
Author technical milestone reports and present results to ARPA-H. Contribute metabolism content to grant proposals and to regulatory engagement on new approach methodologies.
Work with the modeling and integration teams to carry uncertainty from the metabolism models through PBPK to the final organ-level risk.

What success looks like

In the first 6 months, you will have taken ownership of the four models and the team. Metabolite identity and enzyme-isoform predictions will be released on the VHAT platform and reported to ARPA-H. Predicted clearance and kinetic parameters will be passed into the PBPK model with documented methods and scaling assumptions. Prospective predictions on new wet-lab metabolism data will have been locked in advance and scored. A first version of the kinetics model will be in place, and you will have set a clear plan for curation throughput, enzyme coverage and population variability.

By 12 months, the metabolism models will have been tested by an independent validation team on held-out data. Metabolite concentration profiles will feed PBPK for both parent drugs and metabolites, and from there the organ models. Coverage will extend beyond CYP Phase I enzymes, and confidence estimates from the metabolism models will carry through to VHAT risk outputs. The team will be running on a roadmap you set, with each model's strengths and limits clearly documented.

Why join us

This is a chance to lead metabolism science inside a program that links atomic-level predictions to whole-human safety outcomes. The work is hard because the data are scarce, and it matters because toxicity remains a leading cause of clinical failure.

Competitive compensation package with meaningful equity.
Comprehensive health, dental, and vision coverage.
A remote-friendly culture across the U.S. and Europe, with flexible, cross-time-zone working.
Opportunity to shape the future of health, longevity, and our ability to simulate life.
Annual team gatherings and company events.

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More about this job

Responsibilities

Lead a team of computational scientists to build and integrate metabolism models into the VHAT platform for predicting human drug toxicity. Manage the roadmap, validation strategies, and external consortium relationships while ensuring high-quality data curation and scientific rigor.

Requirements

Requires a PhD in a computational or life science field with deep expertise in drug metabolism and computational modeling. Candidates must be proficient in Python and have a strong track record of leading scientific teams and delivering complex projects.

Skills

Computational DMPK
Drug metabolism
Pharmacokinetics
PBPK modeling
Python
Machine learning
Enzyme kinetics
Computational biology
Cheminformatics
Predictive toxicology
Data curation
Scientific leadership
Bayesian inference
ODE-based modeling
Regulatory science
Pharmacology

Benefits

Meaningful equity
Comprehensive health coverage
Dental coverage
Vision coverage
Remote-friendly culture
Annual team gatherings
Company events

Remote locations

United States
Europe
Canada

Remote locations (standardized)

Canada
United States

Visa sponsorship

Not detected in the job text

Keywords

Computational DMPK
Drug Metabolism
Pharmacokinetics
PBPK
Simcyp
GastroPlus
PK-Sim
SimBiology
Python
Machine Learning
Predictive Toxicology
Enzyme Kinetics
Cheminformatics
Computational Biology
Pharmacology
Bayesian Inference
ODE Modeling
Metabolomics
LC-MS
Regulatory Science
ARPA-H
VHAT
Drug-Drug Interactions
Data Curation
Active Learning
Biotechnology
In Silico Modeling
Whole-human Modeling
Phase I Metabolism
Phase II Metabolism

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