50,000 pharmacology-focused training pairs. 16 task types. 5-step trajectory methodology.
From drug interactions to pediatric dosing -- clinical-grade intelligence for pharmaceutical AI.
Every trajectory-enhanced pair follows the same clinical reasoning chain. No shortcuts. No hallucinated conclusions. Each step is verified.
The trajectory methodology ensures your model doesn't just produce answers -- it produces reasoning chains. Every output traces the clinical logic from drug identification through mechanism analysis to a concrete recommendation with monitoring parameters.
Two verified sources. No synthetic-only generation. Textbook ground truth combined with trajectory-enhanced clinical pairs.
Each task type teaches a distinct pharmacological capability. Every pair is trajectory-verified and quality-gated.
Multi-drug interaction assessment, DDI severity grading, contraindication identification, and interaction cascade analysis across polypharmacy regimens.
Receptor binding profiles, signal transduction pathways, molecular target identification, and downstream pharmacodynamic effects at the cellular level.
CYP450 enzyme interactions, phase I/II metabolic pathways, genetic polymorphism effects (CYP2D6, CYP2C19), and metabolite activity profiles.
Therapeutic class analysis, head-to-head efficacy comparison, side effect profiles, cost-effectiveness evaluation, and guideline-based selection criteria.
ADME parameter estimation, dose-response curve analysis, PK/PD modeling, compartmental analysis, and bioavailability calculations across patient populations.
Weight-based dosing calculations, renal/hepatic dose adjustments (CrCl, Child-Pugh), therapeutic window management, and loading/maintenance dose protocols.
TDM protocol design, drug level interpretation (trough/peak), dose titration strategies, narrow therapeutic index management, and monitoring frequency protocols.
Drug delivery system comparison, bioavailability profiling, extended-release vs IR analysis, route of administration selection, and formulation-specific pharmacokinetics.
Side effect profiling, pharmacovigilance signal detection, adverse reaction severity grading, causality assessment (Naranjo scale), and reporting protocol generation.
Black box warning interpretation, REMS program requirements, risk-benefit analysis frameworks, and contraindication assessment for complex patient scenarios.
FDA pregnancy categories, teratogenicity risk assessment, lactation safety evaluation, trimester-specific contraindications, and safer alternative recommendations.
FDA approval pathway analysis (NDA, BLA, 505(b)(2)), labeling requirements, post-market surveillance obligations, and regulatory timeline estimation.
Weight-based dose calculations (mg/kg), age-appropriate formulation selection, developmental pharmacokinetics, and neonatal/infant-specific adjustments.
Beers criteria application, polypharmacy management, age-related PK/PD changes, fall risk assessment from medications, and deprescribing protocols.
Medication adherence strategies, patient education content generation, lifestyle-drug interaction guidance, and health literacy-appropriate communication.
Protocol design methodology, primary/secondary endpoint selection, statistical power calculations, inclusion/exclusion criteria, and adaptive trial frameworks.
Sealed February 28, 2026. Trained on RTX PRO 6000 Blackwell. Zero quantization loss at Q4_K_M.
Tested at both fp16 (Blackwell) and Q4_K_M (3090 Ti via llama-server). Identical accuracy across both precision levels.
Every trajectory-enhanced pair follows the 5-step reasoning chain. This is what your model learns to produce.
Every pharma data order ships with 5 formats, full provenance, and drug interaction lineage in the DATA_CARD.
This is what ships. Every pharma data order. All 16 task types included.
Your team picks the framework -- the data is ready.
All pharma data is sealed in Cloudflare R2 with SHA-256 verification. Frozen snapshots are immutable.