Computational Representations of Protein-ligand Interfaces for Structure-based Virtual Screening.

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Abstract

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification in early drug discovery. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: Herein, the authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement with this method. The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could positively impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered in computational representation.