Graph Regression Based on Graph Autoencoders

Fadlallah, Sarah; Julià, Carme; Serratosa, Francesc

doi:10.1007/978-3-031-23028-8_15

Cited by 5 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been a growth in using neural networks on data represented as graphs across various domains, despite the complexity of graphs that results from their inter-twined characteristics. For the scope of this work, we focus on applications concerning drug potency prediction [25]. The currently used techniques can be divided into four categories: recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks [22].…”

Section: Graph Autoencodersmentioning

confidence: 99%

Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model

Fadlallah

Julià

Garcı́a-Vallvé

et al. 2023

IJMS

View full text Add to dashboard Cite

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.

show abstract

Section: Graph Autoencodersmentioning

confidence: 99%