DrugCombDB

DrugCombDB aggregates curated high-throughput screening and literature-derived drug combination and dose–response data to support analysis and prediction of synergistic and antagonistic effects in cancer research.

Key Features:

• Data Integration from Multiple Sources: Aggregates data from high-throughput screening (HTS) assays, PubMed literature, the FDA Orange Book, and other external databases.
• Extensive Dataset Coverage: Contains over 448,555 drug combinations derived from HTS assays covering 2,887 unique drugs tested across 124 human cancer cell lines.
• Quantitative Dose Responses and Synergy Scores: Includes more than 6 million quantitative dose–response data points and computes multiple synergy scores to evaluate synergistic and antagonistic effects.
• Manual Curation from Literature: Contains manually curated information on 457 drug combinations extracted from PubMed publications.
• Datasets for Computational Modeling: Provides prebuilt datasets prepared for training and evaluating prediction models for both classification and regression analyses.

Scientific Applications:

• Experimental validation of drug combinations: Supports experimental assessment of combination effects and dose–response relationships in cancer cell lines.
• Computational model development and benchmarking: Supplies training and benchmarking datasets for predictive models of synergy and antagonism using classification and regression approaches.
• Pharmacological and translational research: Enables pharmacological modeling and exploration of combination strategies relevant to personalized cancer therapy.

Methodology:

Constructed by combining automated data extraction with manual verification of literature-derived records and HTS assay data, and by computing multiple synergy scores from quantitative dose–response measurements.