Predictive toxicology and regulatory science: QSAR models, exposomics, read-across, and structuring data for REACH, ICH M7 and FDA submissions.
A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.
Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.
Walk through a published tutorial in OPERA + OECD QSAR Toolbox + RDKit and reproduce its results on the provided sample data.
Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README
Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.
Pick one study from Tox21 / ToxCast / ECHA, reproduce the headline result, and write a short technical note on what you found.
Deliverable: GitHub repo + 3-page PDF write-up
Move from tutorials to original analyses on real questions. Start showing your work publicly.
Train and validate a QSAR model on a Tox21 endpoint following OECD principles and document it as a model report for dossier inclusion.
Deliverable: Reproducible repo + OECD-style QMRF + validation plots
Package your work, target real roles, and prepare to interview in the sector.
Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.
Deliverable: Live portfolio URL + PDF CV + cover letter template
Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.
Builds QSAR / read-across models for hazard prediction and regulatory dossiers.
Curates and structures study data for REACH / FDA / EMA submissions.
Answer a short profile and the AI builder will tailor every phase — pace, hours, tools, and resources — to your background and goals in this sector.
Build my personalized roadmap