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

Abstract

Virtual Screening (VS) in drug discovery campaigns deals with enormously large databases containing massive redundant and/or irrelevant features. Thus, the pre-selection process of virtual screening requires developing a fast and precise classification methodology for filtering such a huge database. This paper proposed a framework depending on a wrapper selection approach for features selection. It consists of 1) an optimizer: Gradient-Based Optimizer (GBO), that hybridized with 2) the classifier: k-nearest neighbor (k-NN). The performance of the introduced framework, GBO-kNN, is estimated using real-world benchmark datasets; the QSAR Biodegradation which is composed of 41 features, and the Monoamine Oxidase (MAO) that consisted of 1665 features. the GBO-kNN framework is compared against seven recent swarm intelligence algorithms: Hybrid Harris Hawks Optimization Algorithm (HHO), Grey Wolf Optimization Algorithm (GWO), Butterfly Optimization Algorithm (BOA), Dragonfly Algorithm (DA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), and Salp Swarm Algorithm (SSA) for reaching maximum classification accuracy. The experimental results exhibited that the proposed process, in comparison with other algorithms, achieved high effective accuracy of 98.8% over the high dimensional dataset (MAO) and a moderate effective performance over the low dimensional dataset (QSAR Biodegradation). The proposed framework source code is available at https://codeocean.com/capsule/9906421/tree/v1.