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AtomNet specifications


Unique identifier OMICS_19204
Name AtomNet

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AtomNet citations


Opportunities and obstacles for deep learning in biology and medicine

PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387

[…] d–target complexes and how they represent the 3D structure. The Atomic CNN [] and TopologyNet [] models take 3D structures from PDBBind [] as input, ensuring the ligand–target complexes are reliable. AtomNet [] samples multiple ligand poses within the target binding site, and DeepVS [] and Ragoza et al. [] use a docking program to generate protein–compound complexes. If they are sufficiently accur […]


The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

PMCID: 5355231
PMID: 28029644
DOI: 10.18632/oncotarget.14073

[…] oo inaccurate for systematic binding predictions and physical experiments remain the state of the art for binding determination. In this field, DL-based methods, such as the recently released methods AtomNet based on deep convolutional neural networks [] have allowed to circumvent several limitations and outperform more traditional computational methods including RFs, SVMs for QSAR and ligand-base […]

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