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We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately.
The art and practice of structure-based drug design: A molecular modeling perspective
RS Bohacek, C McMartin, and WC Guida · 1996
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
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Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking
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Pdb-wide collection of binding data: current status of the pdbbind database
Z Liu, Y Li, L Han, J Li, J Liu, Z Zhao, W Nie, Y Liu, and R Wang · 2014
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Deep learning as an opportunity in virtual screening
T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, H Ceulemans, and S Hochreiter · 2014
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Convolutional networks on graphs for learning molecular fingerprints
DK Duvenaud, D Maclaurin, J Iparraguirre, R Bombarell, T Hirzel, A Aspuru-Guzik, and RP Adams · 2015
Cited alongside, same era.
Deep neural nets as a method for quantitative structure–activity relationships
J Ma, RP Sheridan, A Liaw, GE Dahl, and V Svetnik · 2015
Later among the works it cites.
Massively multitask networks for drug discovery
B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, and V Pande · 2015
Later among the works it cites.
I Wallach, M Dzamba, and A Heifets · 2015
Later among the works it cites.
Boosting docking-based virtual screening with deep learning
JC Pereira, ER Caffarena, and C Santos · 2016
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