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Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests.
Current mathematical methods used in qsar/qspr studies
Peixun Liu and Wei Long · 1978
Earlier work this paper cites.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
The role of quantitative structure-activity relationships (qsar) in biomolecular discovery
David A Winkler · 1986
Earlier work this paper cites.
Neural networks in QSAR and drug design
James Devillers · 1996
Earlier work this paper cites.
Can we learn to distinguish between “drug-like” and “nondrug-like” molecules?
Ajay, W. Patrick Walters, and Mark A. Murcko · 1998
Earlier work this paper cites.
Robust qsar models using bayesian regularized neural networks
Frank R. and Burden · 1999
Earlier work this paper cites.
Use of automatic relevance determination in qsar studies using bayesian neural networks
Frank R. Burden, Martyn G. Ford, David C. Whitley, and David A. Winkler · 2000
Earlier work this paper cites.
Random forest: A classification and regression tool for compound classification and qsar modeling
Vladimir Svetnik, Andy Liaw, Christopher Tong, J. Christopher Culberson, Robert P. Sheridan, and Bradley P. Feuston · 2003
Earlier work this paper cites.
Collaborative filtering on a family of biological targets
Dumitru Erhan, Pierre-Jean L’Heureux, Shi Yi Yue, and Yoshua Bengio · 2006
Cited alongside, same era.
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Y. W. Teh · 2006
Cited alongside, same era.
Gaussian processes: a method for automatic qsar modeling of adme properties
Olga Obrezanova, Gábor Csányi, Joelle MR Gola, and Matthew D Segall · 2007
Cited alongside, same era.
Quantitative structure activity relationship model for predicting the depletion percentage of skin allergic chemical substances of glutathione
Hongzong Si, Tao Wang, Kejun Zhang, Yun-Bo Duan, Shuping Yuan, Aiping Fu, and Zhide Hu · 2007
Cited alongside, same era.
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
Ronan Collobert and Jason Weston · 2008
Cited alongside, same era.
Profile-qsar: a novel meta-qsar method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity
Eric Martin, Prasenjit Mukherjee, David Sullivan, and Johanna Jansen · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Later among the works it cites.
Gpu-accelerated machine learning techniques enable qsar modeling of large hts data
E.W. Lowe, M. Butkiewicz, N. Woetzel, and J. Meiler · 2012
Later among the works it cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan Prescott Adams · 2012
Later among the works it cites.
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Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression
Hongying Du, Jie Wang, Zhide Hu, Xiaojun Yao, and Xiaoyun Zhang · 2008
Cited alongside, same era.
Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent · 2010
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey E. Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, and Brian Kingsbury
Cited in the paper.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov
Cited in the paper.
Molecular Operating Environment (MOE)
2013.08 · 2013
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Input warping for bayesian optimization of non-stationary functions
Jasper Snoek, Kevin Swersky, Richard Zemel, and Ryan Prescott Adams · 2013
Later among the works it cites.