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Abstract
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, plasma protein binding (PPB) and also their fragment-specific contributions 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. The updated database and the machine learning tools would accelerate drug discovery, especially in PK/PD process.
