Author:
Ruiz Puentes Paola,Rueda-Gensini Laura,Valderrama Natalia,Hernández Isabela,González Cristina,Daza Laura,Muñoz-Camargo Carolina,Cruz Juan C.,Arbeláez Pablo
Abstract
AbstractDrug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.
Publisher
Springer Science and Business Media LLC
Reference118 articles.
1. Cui, W. et al. Discovering anti-cancer drugs via computational methods. Front. Pharmacol. 11, 72–85 (2020).
2. Lavecchia, A. & Cerchia, C. In silico methods to address polypharmacology: Current status, applications and future perspectives. Drug Discovery Today 21, 288–298 (2016).
3. Thomas, D. et al. Clinical development success rates and contributing factors 2011–2020 (2021).
4. Food, T. & Administration, D. Fda executive summary (2017).
5. Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献