Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .
Keywords: Bioactive peptide; Computational peptidology; PepQSAR database; Quantitative structure–activity relationship; Statistical modeling.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.