Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2105
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions ; volume:22 ; number:1 ; day:8 ; month:11 ; year:2021 ; pages:1-15 ; date:12.2021
BMC bioinformatics ; 22, Heft 1 (8.11.2021), 1-15, 12.2021
- Creator
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Seo, Sangmin
Choi, Jonghwan
Park, Sanghyun
Ahn, Jaegyoon
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s12859-021-04466-0
- URN
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urn:nbn:de:101:1-2022012120095877275582
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:21 AM CEST
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Associated
- Seo, Sangmin
- Choi, Jonghwan
- Park, Sanghyun
- Ahn, Jaegyoon
- 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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Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
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
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Functional dynamics of protein-ligand interactions
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
Ensembling methods for protein-ligand binding affinity prediction
Learning protein-ligand binding affinity with atomic environment vectors
Protein-ligand interactions
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3
Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
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
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Functional dynamics of protein-ligand interactions