In:
Current Computer-Aided Drug Design, Bentham Science Publishers Ltd., Vol. 20, No. 1 ( 2024-02), p. 33-41
Kurzfassung:
There has been a growing interest in discovering a viable drug for the new coronavirus
(SARS-CoV-2) since the beginning of the pandemic. Protein-ligand interaction studies are a crucial step in the drug discovery process, as it helps us narrow the search space for potential ligands with
high drug-likeness. Derivat ives of popular drugs like Remdesivir generated through tools employing evolutionary
algorithms are usually considered potential candidates. However, screening promising molecules from such a large search space is difficult. In a conventional screening process, for each ligand-target pair,
there are time-consuming interaction studies that use docking simulations before downstream tasks like thermodynamic, kinetic, and electrostatic-potential evaluation. Objective: This work aims to build a model based on deep learning applied over the graph structure of the
molecules to accelerate the screening process for novel potential candidates for SARS-CoV-2 by predicting the binding energy of the protein-ligand complex. Methods: In this work, ‘Graph Convolutional Capsule Regression’ (GCCR), a model which uses Capsule
Neural Networks (CapsNet) and Graph Convolutional Networks (GCN) to predict the binding energy of a protein-ligand complex is being proposed. The model’s predictions were further validated with kinetic and
free energy studies like Molecular Dynamics (MD) for kinetic stability and MM/GBSA analysis for free energy calculations. Results: The GCCR showed an RMSE value of 0.0978 for 81.3% of the concordance index. The RMSE
of GCCR converged around the iteration of just 50 epochs scoring a lower RMSE than GCN and GAT. When training with Davis Dataset, GCCR gave an RMSE score of 0.3806 with a CI score of 87.5%. Conclusion: The proposed GCCR model shows great potential in improving the screening process based
on binding affinity and outperforms baseline machine learning models like DeepDTA, KronRLS, Sim- Boost, and other Graph Neural Networks (GNN) based models like Graph Convolutional Networks
(GCN) and Graph Attention Networks (GAT).
Materialart:
Online-Ressource
ISSN:
1573-4099
DOI:
10.2174/1573409919666230331083953
Sprache:
Englisch
Verlag:
Bentham Science Publishers Ltd.
Publikationsdatum:
2024
SSG:
15,3
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