PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity ; volume:15 ; number:1 ; day:2 ; month:3 ; year:2023 ; pages:1-21 ; date:12.2023
Journal of cheminformatics ; 15, Heft 1 (2.3.2023), 1-21, 12.2023
- Creator
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s13321-023-00701-3
- URN
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urn:nbn:de:101:1-2024011908480116768786
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:32 AM CEST
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Associated
- Ammar, Ammar
- Cavill, Rachel
- Evelo, Chris T.
- Willighagen, Egon
- SpringerLink (Online service)
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Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
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CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
PSnpBind: a database of mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow
AI-based prediction of protein–ligand binding affinity and discovery of potential natural product inhibitors against ERK2
ERL‐ProLiGraph: Enhanced representation learning on protein‐ligand graph structured data for binding affinity prediction
Application of QM/MM methods to Protein/Ligand binding
Ensembling methods for protein-ligand binding affinity prediction
Learning protein-ligand binding affinity with atomic environment vectors
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
PSnpBind: a database of mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow
AI-based prediction of protein–ligand binding affinity and discovery of potential natural product inhibitors against ERK2
ERL‐ProLiGraph: Enhanced representation learning on protein‐ligand graph structured data for binding affinity prediction