Learning from the ligand: using ligand-based features to improve binding affinity prediction
Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand...
Autors principals: | Boyles, F, Deane, C, Morris, G |
---|---|
Format: | Journal article |
Publicat: |
2019
|
Ítems similars
-
Learning from the ligand: using ligand-based features to improve binding affinity prediction
per: Boyles, F, et al.
Publicat: (2019) -
Protein-ligand interaction graphs: Learning from ligand-shaped 3D interaction graphs to improve binding affinity prediction
per: Moesser, M, et al.
Publicat: (2022) -
Learning from docked ligands: ligand-based features rescue structure-based scoring functions when trained on docked poses
per: Boyles, F, et al.
Publicat: (2021) -
Prediction of protein-ligand binding affinity with deep learning
per: Yuxiao Wang, et al.
Publicat: (2023-01-01) -
Learning protein-ligand binding affinity with atomic environment vectors
per: Meli, R, et al.
Publicat: (2021)