Protein-Ligand Affinity Prediction
3 papers with code • 2 benchmarks • 1 datasets
PDBBind
Most implemented papers
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
agave233/SIGN
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To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
XieResearchGroup/Physics-aware-Multiplex-GNN
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On small molecule dataset for predicting quantum chemical properties, PaxNet reduces the prediction error by 15% and uses 73% less memory than the best baseline.
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
trrt-good/WELP-PLAPT
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bioRxiv 2024
Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently.
