Paper Info
Title
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 their computational efficiency and incorporate an AD. Two modifications are proposed: (i) using vanishing kernels (i.e., kernel functions with a finite support) and (ii) using the Tanimoto distance between molecular fingerprints as a radial basis function. This construction is termed "Vanishing Ranking Kernels" (VRK). Using VRK on 21 HTS assays, it is shown that VRK can compete in performance with a graph convolutional deep neural network. VRK are conceptually simple and fast to train. During training, they require optimizing a single parameter. A trained VRK model usually defines an active AD. Exploiting this AD can significantly increase the screening frequency of a VRK model. Software: https://github.com/UnixJunkie/rankers. Data sets: https://zenodo.org/record/ 1320776 and https://zenodo.org/record/3540423.
Year
DOI
Venue
2020
10.1021/acs.jcim.9b01075
JOURNAL OF CHEMICAL INFORMATION AND MODELING
DocType
Volume
Issue
Journal
60
9
ISSN
Citations 
PageRank 
1549-9596
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Francois Berenger1234.11
Yoshihiro Yamanishi2126883.44