Director/AD, Computational DMPK (Drug Metabolism & Pharmacokinetics)

Deep Origin

3d • Remote

About The Position

Deep Origin is a biotechnology company building AI-powered platforms to simulate biology, streamline life science research and model complex biological systems. The company leads the PREDICTS consortium, funded by ARPA-H under the CATALYST program, to develop in silico models for predicting human drug safety and toxicity.

The Director/AD, Computational DMPK will lead the Metabolism group building VHAT (Virtual Human Avatars of Toxicology), a platform that predicts organ-level human toxicity from a molecule, dose and patient population. The role owns four connected models: drug-metabolizing enzyme, site of metabolism, metabolite structure and enzyme kinetics. The leader will mentor the team, set scientific direction and integrate predicted metabolism parameters into the physiologically based pharmacokinetic (PBPK) model that predicts exposure across VHAT.

The role also owns the metabolism-to-PBPK interface, model validation, data strategy and model release roadmap. It includes extending enzyme and pathway coverage beyond CYP Phase I metabolism, carrying uncertainty through PBPK to organ-level risk outputs, and working with consortium partners, experimental collaborators, validation teams and funders. Applicants must be authorized to work for any U.S. employer; Deep Origin cannot sponsor or take over sponsorship of an employment visa at this time.

Requirements

• PhD in computational chemistry, cheminformatics, drug metabolism, computational biology, pharmacology or a related field.
• Deep, hands-on expertise in drug metabolism, including CYP and non-CYP enzymes, sites and products of metabolism, enzyme kinetics and clearance, and the ability to assess whether predictions are chemically and biologically sound.
• Strong track record in computational modeling as a model builder or expert end-user, including machine learning for molecular property or reactivity prediction and mechanistic or kinetic modeling.
• Experience with ODE-based models, parameter estimation, and Bayesian or frequentist inference.
• Fluency in Python.
• Demonstrated rigor in model validation, including appropriate data splits, applicability domains and external or blinded benchmarks, and the ability to explain model limitations.
• Experience leading a scientific team and delivering against milestones and deadlines.
• Clear written and spoken communication with scientists, partners and funders.
• Authorization to work for any employer in the U.S.; employment visa sponsorship or transfer is not available.

Nice To Haves

• PBPK experience, which is strongly preferred.
• 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 IVIVE scaling factors and their limitations.
• 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 uncertainty into downstream predictions.
• 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 addressing metabolism liabilities during lead optimization.

Responsibilities

• Lead computational scientists and data curators across sites; set scientific direction, priorities and review standards, and develop team members.
• Own the roadmap and release gates for the four metabolism models, determining readiness for the VHAT platform, standalone products and external review.
• Lead integration of metabolism models with PBPK, defining parameter transfer, clearance scaling and checks against clinical PK and drug-drug interaction data.
• Work with PBPK modelers to ensure parent and metabolite exposure flows correctly into organ models.
• Design rigorous validation experiments and processes, including leakage-aware data splits, applicability-domain and confidence reporting, prospectively locked predictions, and external, blinded and independent benchmarks.
• Set the data strategy for scarce high-quality labels, including curation targets and throughput, AI-assisted literature mining with expert quality control, data licensing support and wet-lab experiment design with partners.
• Design active-learning compound selection and experiments using recombinant enzymes, human liver microsomes and hepatocytes.
• Extend model coverage beyond CYP Phase I metabolism to Phase II enzymes (UGT, SULT), FMO, AOX and multistep metabolic networks; scope enzyme polymorphism and population variability.
• Manage 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 regulatory engagement on new approach methodologies.
• Work with modeling and integration teams to carry uncertainty from metabolism models through PBPK to organ-level risk.

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: Ph.D. or professional degree