Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1546-1696
- 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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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning ; volume:40 ; number:10 ; day:23 ; month:5 ; year:2022 ; pages:1520-1527 ; date:10.2022
Nature biotechnology ; 40, Heft 10 (23.5.2022), 1520-1527, 10.2022
- Creator
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Rube, H. Tomas
Rastogi, Chaitanya
Feng, Siqian
Kribelbauer, Judith F.
Li, Allyson
Becerra, Basheer
Melo, Lucas A. N.
Do, Bach Viet
Li, Xiaoting
Adam, Hammaad H.
Shah, Neel H.
Mann, Richard S.
Bussemaker, Harmen J.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41587-022-01307-0
- URN
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urn:nbn:de:101:1-2022122321080867227926
- 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
Data provider
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Associated
- Rube, H. Tomas
- Rastogi, Chaitanya
- Feng, Siqian
- Kribelbauer, Judith F.
- Li, Allyson
- Becerra, Basheer
- Melo, Lucas A. N.
- Do, Bach Viet
- Li, Xiaoting
- Adam, Hammaad H.
- Shah, Neel H.
- Mann, Richard S.
- Bussemaker, Harmen J.
- SpringerLink (Online service)
Other Objects (12)
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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
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
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
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
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
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
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Functional dynamics of protein-ligand interactions