Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors
PN Shiammala, NKB Duraimutharasan, B Vaseeharan… - Methods, 2023 - Elsevier
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides
opportunities to discover and develop innovative drugs. The use of AI in drug discovery is …
opportunities to discover and develop innovative drugs. The use of AI in drug discovery is …
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions
are mis-calibrated and lack interpretability, limiting their adoption in critical applications. To …
are mis-calibrated and lack interpretability, limiting their adoption in critical applications. To …
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
A Fluetsch, E Di Lascio, G Gerebtzoff… - Molecular …, 2024 - ACS Publications
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML)
models that relate the molecular structure to compound properties. Such quantitative …
models that relate the molecular structure to compound properties. Such quantitative …
Harnessing chemical space neural networks to systematically annotate GPCR ligands
FG Hansson, NG Madsen, LG Hansen, T Jakociunas… - BioRxiv, 2024 - biorxiv.org
Drug-target interaction databases comprise millions of manually curated data points, yet
there are missed opportunities for repurposing established interaction networks to infer …
there are missed opportunities for repurposing established interaction networks to infer …