Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included

Abstract
In ligand-based virtual screening, high-throughput screening (HTS) data sets can be exploited to train classification models. Such models can be used to prioritize yet untested molecules, from the most likely active (against a protein target of interest) to the least likely active. In this study, a single-parameter ranking method with an Applicability Domain (AD) is proposed. In effect, Kernel Density Estimates (KDE) are revisited to improve...
Paper Details
Title
Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included
Published Date
Apr 13, 2020
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