Pharmaceutical R&D & Drug Discovery
Accelerating the pipeline from molecule to medicine with AI-ready biomedical data
Industry Challenge
AI is transforming pharmaceutical R&D — from target identification and molecular property prediction to clinical trial design and regulatory submission. These models require diverse, expertly annotated biomedical data: scientific literature, chemical structures, assay results, clinical trial reports, and adverse event records — all labeled to the precision that drug discovery demands.
How SCILabel Serves This Industry
Data Collection
We source biomedical literature datasets (PubMed abstracts, full-text papers), adverse event report corpora (FAERS-derived, de-identified), chemical compound-activity datasets, and clinical trial document collections from academic and pharmaceutical research partners.
Data Annotation & Labeling
Our Track 4 (Genomics & Biomedical) and Track 1 (Medical NLP) workforce annotates biomedical text with chemical entity recognition (drug names, molecular targets, chemical structures), gene/protein entity tagging, drug–disease–gene relation extraction, adverse event term normalisation (MedDRA), and clinical trial eligibility criterion annotation.
Data & Model Evaluation
Evaluators benchmark biomedical NLP model performance on named entity recognition and relation extraction using BioCreative and other standard benchmarks, and assess pharmacovigilance model signal detection against known adverse event ground truth.
Annotation Types & Formats
- Chemical entity recognition: drug names, molecular targets, SMILES notation
- Gene and protein entity tagging with UniProt/HGNC normalisation
- Drug–disease–gene relation extraction
- Adverse event term annotation and MedDRA coding
- Clinical trial eligibility criterion classification
- Biomedical literature document classification
Specialist Workforce Tracks
Track 4 (Genomics & Biomedical) and Track 1 (Medical NLP): Pharmacologists, Pharmacists, Biochemists, Biotechnologists, Clinical Researchers.