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This article presents a comprehensive and user-friendly protocol for building and rigorously evaluating target-specific machine-learning scoring functions (SFs) for structure-based virtual screening (SBVS) via docking. The protocol is organized into four sections and includes example targets, code, and input/output data. The aim is to provide guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules.
