GBO-kNN a new framework for enhancing the performance of ligand-based virtual screening for drug discovery

作者：

Highlights：

• GBO-kNN is a proposed framework based on a wrapper approach for features selection.

• The aim of the research is to reach maximum classification accuracy.

• The performance of GBO-kNN is evaluated against real benchmark datasets.

• The GBO-kNN performance is compared against seven recent metaheuristic algorithms.

• Results showed high effectiveness on one dataset and moderate on another.

摘要

•GBO-kNN is a proposed framework based on a wrapper approach for features selection.•The aim of the research is to reach maximum classification accuracy.•The performance of GBO-kNN is evaluated against real benchmark datasets.•The GBO-kNN performance is compared against seven recent metaheuristic algorithms.•Results showed high effectiveness on one dataset and moderate on another.

论文关键词：Virtual screening (VS),Classification accuracy,Feature selection,Gradient-Based Optimizer (GBO),k-Nearest Neighbors (k-NN)

论文评审过程：Received 14 July 2021, Revised 4 February 2022, Accepted 21 February 2022, Available online 26 February 2022, Version of Record 3 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116723