Introduction: Anticancer peptides (ACPs) promise low toxicity, enhanced specificity, and an ability to defeat drug resistance. However, the ACP data are scattered across databases and buried in literature, limiting accessibility and utility. To resolve this, we established dbACP, an extensive database designed to properly organize ACP information and facilitate drug research and development. Methods: ACPs' data were curated from different established databases and literature. Data were preprocessed and deduplicated to ensure accuracy and reliability of the data. The database was created using MySQL and with a Flask-based web interface. Biopython and RDKit were used to calculate molecular descriptors and physicochemical parameters. ADMET prediction and structural modeling were further performed using ADMET-AI and ESMFold. Results: dbACP (https://dbacp.iiita.ac.in) contains 8,439 curated entries from 682 distinct sources involving 750 cancer cell lines and 207 cancer types. Each entry includes detailed annotation such as General Description of ACP, Sequence-based Information, Activity Information, Physicochemical Properties, Structural Information, Molecular Descriptors, and ADMET Properties information. This platform offers simple as well as advanced search, browse, and download functionality to enable data exploration and analysis. Discussion: By amalgamating experimental and computational data, dbACP mitigates significant deficiencies in current ACP resources. The incorporation of structural and pharmacokinetic characteristics improves its utility for peptide design, QSAR modeling, and machine learning methodologies. Observed trends in amino acid composition and peptide characteristics offer additional understanding of ACP mechanisms and optimization approaches. Conclusion: dbACP serves as a centralized database that simplifies ACP discovery and accelerates the development of peptide-based cancer therapies.
Tripathi et al. (2026) studied this question.