Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs

Frontiers in Chemistry

Chemistry

Natiele Carla da Silva Ferreira¹  Evellyn Araujo Dias¹  Victor Maricato¹  Anael Viana Pinto Alberto¹  Luiz Anastacio Alves¹  Nt Jose Aguiar Coelho² 

[1] Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil;National Institute of Industrial Property - INPI and Veiga de Almeida University - UVA, Rio de Janeiro, Brazil;

关键词: GNN;    deep learning;    drug discovery;    virtual screening;    natural products;   

DOI  :  10.3389/fchem.2021.787194

 received in 2021-10-18, accepted in 2021-12-10,  发布年份 2022

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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Copyright © 2022 Alves, Ferreira, Maricato, Alberto, Dias and Jose Aguiar Coelho.