DeepDTAF: a deep learning method to predict protein–ligand binding affinity
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Title
DeepDTAF: a deep learning method to predict protein–ligand binding affinity
Authors
Keywords
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Journal
BRIEFINGS IN BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2021-02-23
DOI
10.1093/bib/bbab072
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- (2018) Marta M Stepniewska-Dziubinska et al. BIOINFORMATICS
- OUP accepted manuscript
- (2018) BIOINFORMATICS
- RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy
- (2018) Stephen K Burley et al. NUCLEIC ACIDS RESEARCH
- HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy
- (2017) Yumeng Yan et al. NUCLEIC ACIDS RESEARCH
- TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions
- (2017) Zixuan Cang et al. PLoS Computational Biology
- Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
- (2017) Anna Cichonska et al. PLoS Computational Biology
- Computational protein–ligand docking and virtual drug screening with the AutoDock suite
- (2016) Stefano Forli et al. Nature Protocols
- RaptorX-Property: a web server for protein structure property prediction
- (2016) Sheng Wang et al. NUCLEIC ACIDS RESEARCH
- A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL
- (2016) Prudence Mutowo et al. Journal of Biomedical Semantics
- Towards dropout training for convolutional neural networks
- (2015) Haibing Wu et al. NEURAL NETWORKS
- SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity
- (2014) C. N. Magnan et al. BIOINFORMATICS
- Toward more realistic drug-target interaction predictions
- (2014) T. Pahikkala et al. BRIEFINGS IN BIOINFORMATICS
- Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features
- (2014) Dong-Sheng Cao et al. Molecular Informatics
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- (2013) Murat Can Cobanoglu et al. Journal of Chemical Information and Modeling
- SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications
- (2013) Mengyao Zhao et al. PLoS One
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- (2012) Dong-Sheng Cao et al. ANALYTICA CHIMICA ACTA
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- (2012) Ruth Nussinov et al. BMC BIOLOGY
- LigPlot+: Multiple Ligand–Protein Interaction Diagrams for Drug Discovery
- (2011) Roman A. Laskowski et al. Journal of Chemical Information and Modeling
- Ligand binding to protein-binding pockets with wet and dry regions
- (2011) L. Wang et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Open Babel: An open chemical toolbox
- (2011) Noel M O'Boyle et al. Journal of Cheminformatics
- Targeting innate immunity protein kinase signalling in inflammation
- (2009) Matthias Gaestel et al. NATURE REVIEWS DRUG DISCOVERY
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