Abstract

Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.

Highlights

  • Mathematic modeling comprises a valuable important tool in the development of the pharmacology and chemistry fields since their beginning as formal disciplines (Gaddum, 1953; Atkinson and Lalonde, 2007; Finlay et al, 2020)

  • This review focus on a subtype of deep learning algorithm named graph neural network (GNN), currently one of the most applied

  • The studies pointed out in this paper indicate that the use of Graph neural network (GNN) in virtual screening (VS) may guide the drug discovery process

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Summary

Introduction

Mathematic modeling comprises a valuable important tool in the development of the pharmacology and chemistry fields since their beginning as formal disciplines (Gaddum, 1953; Atkinson and Lalonde, 2007; Finlay et al, 2020). This review focus on a subtype of deep learning algorithm named graph neural network (GNN), currently one of the most applied.

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