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Found 2 papers, 1 papers with code
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions.
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
These models are further adapted for regression tasks to predict experimental binding affinities and pIC50 is crucial for drugs potency and efficacy.
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