Distance plus attention for binding affinity prediction

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Distance plus attention for binding affinity prediction ; volume:16 ; number:1 ; day:12 ; month:5 ; year:2024 ; pages:1-17 ; date:12.2024
Journal of cheminformatics ; 16, Heft 1 (12.5.2024), 1-17, 12.2024

Creator: Rahman, Julia
Newton, M. A. Hakim
Ali, Mohammed Eunus
Sattar, Abdul

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13321-024-00844-x

URN: urn:nbn:de:101:1-2407221026586.102185753581

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:54 AM CEST

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Rahman, Julia
Newton, M. A. Hakim
Ali, Mohammed Eunus
Sattar, Abdul
SpringerLink (Online service)

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Multi-View Kernel Methods for Binding Affinity Prediction

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DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Explainable deep drug–target representations for binding affinity prediction

ISLAND: in-silico proteins binding affinity prediction using sequence information

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

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Structure-based, deep-learning models for protein-ligand binding affinity prediction

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

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Correcting the impact of docking pose generation error on binding affinity prediction

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Multi-View Kernel Methods for Binding Affinity Prediction

Ensembling methods for protein-ligand binding affinity prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Explainable deep drug–target representations for binding affinity prediction

ISLAND: in-silico proteins binding affinity prediction using sequence information

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Structure-aware deep model for MHC-II peptide binding affinity prediction

Structure-based, deep-learning models for protein-ligand binding affinity prediction

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Correcting the impact of docking pose generation error on binding affinity prediction

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Distance plus attention for binding affinity prediction

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Other Objects (12)

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Multi-View Kernel Methods for Binding Affinity Prediction

Ensembling methods for protein-ligand binding affinity prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Explainable deep drug–target representations for binding affinity prediction

ISLAND: in-silico proteins binding affinity prediction using sequence information

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Structure-aware deep model for MHC-II peptide binding affinity prediction

Structure-based, deep-learning models for protein-ligand binding affinity prediction

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Correcting the impact of docking pose generation error on binding affinity prediction

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Multi-View Kernel Methods for Binding Affinity Prediction

Ensembling methods for protein-ligand binding affinity prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Explainable deep drug–target representations for binding affinity prediction

ISLAND: in-silico proteins binding affinity prediction using sequence information

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Structure-aware deep model for MHC-II peptide binding affinity prediction

Structure-based, deep-learning models for protein-ligand binding affinity prediction

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Correcting the impact of docking pose generation error on binding affinity prediction

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Multi-View Kernel Methods for Binding Affinity Prediction

Ensembling methods for protein-ligand binding affinity prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Explainable deep drug–target representations for binding affinity prediction

ISLAND: in-silico proteins binding affinity prediction using sequence information

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Structure-aware deep model for MHC-II peptide binding affinity prediction

Structure-based, deep-learning models for protein-ligand binding affinity prediction

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Correcting the impact of docking pose generation error on binding affinity prediction

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