Director​/AD, Computational DMPK; Drug Metabolism & Pharmacokinetics

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.

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.

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, Gastro Plus, PK‑Sim, Sim Biology 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.

• 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…