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In this work, we propose a deep learning approach to improve docking-based virtual screening.
Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning Representations by Back-Propagating Errors. Cognitive Modeling 1988
1988
Earlier work this paper cites.
Waibel, A.; Hanazawa, T.; Hinton, G.; Shikano, K.; Lang, K. J. Phoneme Recognition Using Time-Delay Neural Networks. Acoustics, Speech and Signal Processing, IEEE Transactions on 1989
1989
Earlier work this paper cites.
Walters, W.; Stahl, M. T.; Murcko, M. A. Virtual Screening - An Overview. Drug Discovery Today 1998
1998
Earlier work this paper cites.
Bissantz, C.; Folkers, G.; Rognan, D. Protein-Based Virtual Screening of Chemical Databases. 1. Evaluation of Different Docking/Scoring Combinations. Journal of Medicinal Chemistry 2000
2000
Earlier work this paper cites.
Shoichet, B. K. Virtual Screening of Chemical Libraries. Nature 2004
2004
Earlier work this paper cites.
Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and Scoring in Virtual Screening for Drug Discovery: Methods and Applications. Nature Reviews Drug Discovery 2004
2004
Earlier work this paper cites.
Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. UCSF Chimera - A Visualization System for Exploratory esearch and Analysis. Journal of Computational Chemistry 2004
2004
Earlier work this paper cites.
Ghosh, S.; Nie, A.; An, J.; Huang, Z. Structure-Based Virtual Screening of Chemical Libraries for Drug Discovery. Curr Opin Chem Biol 2006
2006
Earlier work this paper cites.
Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking Sets for Molecular Docking. Journal of medicinal chemistry 2006
2006
Earlier work this paper cites.
Nicholls, A. What do We Know and when do We Know It? Journal of Computer-Aided Molecular Design 2008
2008
Earlier work this paper cites.
Hecht, D.; Fogel, G. B. Computational Intelligence Methods for Docking Scores. Current Computer-Aided Drug Design 2009
2009
Earlier work this paper cites.
Bengio, Y. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning 2009
2009
Earlier work this paper cites.
Lang, P. T.; Brozell, S. R.; Mukherjee, S.; Pettersen, E. F.; Meng, E. C.; Thomas, V.; Rizzo, R. C.; Case, D. A.; James, T. L.; Kuntz, I. D. DOCK 6: Combining Techniques to Model RNA–Small Molecule Complexes. Rna 2009
2009
Earlier work this paper cites.
Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of Computational Chemistry 2009
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
Schneider, G. Virtual Screening: An Endless Staircase? Nature Reviews Drug Discovery 2010
2010
Cited alongside, same era.
Durrant, J. D.; McCammon, J. A. NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein-Ligand Complexes. Journal of Chemical Information and Modeling 2010
2010
Cited alongside, same era.
Ballester, P. J.; Mitchell, J. B. A Machine Learning Approach to Predicting Protein–Ligand Binding Affinity with Applications to Molecular Docking. Bioinformatics 2010
2010
Cited alongside, same era.
Bergstra, J.; Breuleux, O.; Bastien, F.; Lamblin, P.; Pascanu, R.; Desjardins, G.; Turian, J.; Warde-Farley, D.; Bengio, Y. Theano: a CPU and GPU Math Expression Compiler. Proceedings of the Python for Scientific Computing Conference (SciPy). 2010; p 3
2010
Cited alongside, same era.
Durrant, J. D.; Friedman, A. J.; Rogers, K. E.; McCammon, J. A. Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening. Journal of Chemical Information and Modeling 2013
2013
Later among the works it cites.
Bengio, Y.; Courville, A.; Vincent, P. Representation Learning: A Review and New Perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2013
2013
Later among the works it cites.
Lusci, A.; Pollastri, G.; Baldi, P. Deep Architectures and Deep Learning in Chemoinformatics: the Prediction of Aqueous Solubility for Drug-like Molecules. Journal of Chemical Information and Modeling 2013
2013
Later among the works it cites.
Weber, J.; Achenbach, J.; Moser, D.; Proschak, E. VAMMPIRE: a Matched Molecular Pairs Database for Structure-Based Drug Design and Optimization. Journal of medicinal chemistry 2013
2013
Later among the works it cites.
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Armstrong, M. S.; Morris, G. M.; Finn, P. W.; Sharma, R.; Moretti, L.; Cooper, R. I.; Richards, W. G. ElectroShape: Fast Molecular Similarity Calculations Incorporating Shape, Chirality and Electrostatics. Journal of Computer-aided Molecular Design 2010
2010
Cited alongside, same era.
Trott, O.; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry 2010
2010
Cited alongside, same era.
Arlot, S.; Celisse, A. A Survey of Cross-Validation Procedures for Model Selection. Statistics Surveys 2010
2010
Cited alongside, same era.
Kinnings, S. L.; Liu, N.; Tonge, P. J.; Jackson, R. M.; Xie, L.; Bourne, P. E. A Machine Learning-Based Method to Improve Docking Scoring Functions and Its Application to Drug Repurposing. Journal of Chemical Information and Modeling 2011
2011
Cited alongside, same era.
Durrant, J. D.; McCammon, J. A. NNScore 2.0: A Neural-Network Receptor-Ligand Scoring Function. Journal of Chemical Information and Modeling 2011
2011
Cited alongside, same era.
Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; Kuksa, P. Natural Language Processing (Almost) from Acratch. The Journal of Machine Learning Research 2011
2011
Cited alongside, same era.
Jahn, A.; Rosenbaum, L.; Hinselmann, G.; Zell, A. 4D Flexible Atom-Pairs: An Efficient Probabilistic Conformational Space Comparison for Ligand-Based Virtual Screening. J. Cheminformatics 2011
2011
Cited alongside, same era.
Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S. Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review. The AAPS journal 2012
2012
Cited alongside, same era.
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; Dean, J. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS) 2013
2013
Later among the works it cites.
Arciniega, M.; Lange, O. F. Improvement of Virtual Screening Results by Docking Data Feature Analysis. Journal of Chemical Information and Modeling 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
Unterthiner, T.; Mayr, A.; Klambauer, G.; Steijaert, M.; Wegner, J. K.; Ceulemans, H.; Hochreiter, S. Deep Learning as an Opportunity in Virtual Screening. Proceedings of the Deep Learning Workshop at NIPS. 2014
2014
Later among the works it cites.
dos Santos, C. N.; Gatti, M. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. COLING. 2014; pp 69–78
2014
Later among the works it cites.
dos Santos, C. N.; Zadrozny, B. Learning Character-Level Representations for Part-of-Speech Tagging. Proceedings of the 31st International Conference on Machine Learning (ICML-14). 2014; pp 1818–1826
2014
Later among the works it cites.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems. 2015; pp 2215–2223
2015
Later among the works it cites.