Aptan.ai provides end-to-end AI data services tailored to pharmacology and life-science innovation, enabling the creation of safe, effective, and scientifically reliable AI solutions for drug discovery, precision therapeutics, pharmacovigilance, and clinical pharmacology. Our services combine domain expertise, advanced annotation workflows, and rigorous compliance to power scalable and trustworthy pharmacology AI models.
What We Provide
Drug & Molecular Data Annotation
- Labeling of small molecules, biologics, peptides, and drug-like compounds
- Annotation of molecular properties such as solubility, bioavailability, toxicity, and metabolism
- Structuring SMILES, InChI, molecular graphs, and compound descriptors
- Binding site and protein-ligand interaction labeling for model training
Pharmacokinetics (PK) & Pharmacodynamics (PD) Labeling
- Annotation of ADME datasets (Absorption, Distribution, Metabolism, Excretion)
- Labeling dose-response relationships, therapeutic index, half-life, clearance, and volume of distribution
- PD annotation for drug efficacy, receptor activity, and biomarker response modeling
- Support for AI-driven personalized dosing and response prediction
Clinical Pharmacology & Trial Data Structuring
- Labeling of drug trial protocols, clinical outcomes, and therapy response data
- Structuring lab parameters (renal, hepatic, hematology, electrolytes) linked to drug effects
- Annotation of comorbidities, co-medications, contraindications, and drug interactions
- Longitudinal annotation for treatment outcome and efficacy analysis
Pharmacovigilance & Safety Data Labeling
- Annotation of adverse drug reactions (ADRs), side-effect reports, and case safety narratives
- Classification of severity, causality, and event outcomes
- Labeling post-market surveillance, signal detection, and risk association data
- Support for AI-based drug safety monitoring and regulatory submissions
Prescription & Formulary Data Labeling
- Labeling drug categories, mechanisms of action, dosage forms, and administration routes
- Annotation of therapeutic guidelines, formulary restrictions, and treatment pathways
- Structuring brand-generic relationships and drug substitution mapping
- AI training support for clinical decision support and e-prescription validation
Multi-Modal Pharmacology Dataset Alignment
- Integration of molecular data, clinical parameters, trial outcomes, and safety narratives
- Context-rich labeling to support LLMs and foundation pharmacology AI models
- Harmonized datasets for explainable and context-aware model development
Quality Assurance & Scientific Validation
- Pharmacologist and clinician-led expert review
- Multi-stage QA for accuracy, consistency, and scientific reliability
- Secure, compliant, and audit-ready dataset delivery
- Dataset packages optimized for AI training, validation, and regulatory compliance
Key Use Cases
- Drug toxicity and side-effect prediction
- AI-powered pharmacovigilance and signal detection
- Drug-drug interaction (DDI) modeling
- Personalized dosing and treatment response prediction
- AI-assisted drug discovery and molecular screening
- Regulatory safety AI systems and decision support tools
With scientifically validated data and scalable annotation pipelines, Aptan.ai accelerates pharmacology AI development that researchers trust, regulators accept, and clinicians rely on.