Computational modeling study uncovers AI-driven binding affinity predictions for human serum albumin, highlighting expanded resources for drug discovery and structural analysis.
Human serum albumin (HSA) as the most prevalent protein constitutes ~60% of the protein mass. In this work, we augment our previously released HSA database (HSADab) via the incorporation of AI-powered modelling. The constructed webserver www.hsadab.cn enables instant prediction of HSA binding affinities for drug-like molecules through various machine-learning predictors, hosts the most comprehensive affinity and structure banks containing all HSA-relevant data published so far, and contains a complete set of deep-learning assisted docking structures for molecules presented in the database. We additionally present comprehensive analyses on the protein conformational space, docking performance and AlphaFold modelling, and further open source the most robust fingerprints-based model in the GitHub repository https://github.com/proszxppp/HSADab.
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Zheng et al. (2025) studied this question.
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