Plasma protein binding has a profound impact on the pharmacodynamic and pharmacokinetic properties of drug candidates and thus is integral to drug discovery. Nevertheless, extant methods to examine small-molecule interactions with plasma protein have various limitations, thus creating a need for alternative methods. Herein we present a comprehensive and cross-validated in silico workflow for the prediction of small-molecule binding to Human Serum Albumin (HSA), the most ubiquitous plasma protein. This protocol reliably predicts small-molecule interactions with HSA including binding affinity via a multiple linear regression, binding site using a na�ve Bayesian classifier, and binding pose using induced fit docking. Further, this workflow is implemented in a portable and automated format that can be downloaded and used by other end users either as is or with customization.

Automated ligand and structure based protocol for in-silico prediction of human serum albumin binding

248th National Meeting (2014)

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