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In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients.
Proteochemometrics: a tool for modeling the molecular interaction space
J. Wikberg, M. Lapinsh, and P. Prusis · 2004
Earlier work this paper cites.
The nci60 human tumour cell line anticancer drug screen
R. H. Shoemaker · 2006
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Prediction of drug-target interaction networks from the integration of chemical and genomic spaces
Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa · 2008
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Extended-connectivity fingerprints
D. Rogers and M. Hahn · 2010
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Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set
I. Sushko, S. Novotarskyi, R. Körner, A. K. Pandey, A. Cherkasov, J. Li, P. Gramatica, K. Hansen, T. Schroeter, and K.-R. Müller · 2010
Earlier work this paper cites.
Comprehensive analysis of kinase inhibitor selectivity
M. I. Davis, J. P. Hunt, S. Herrgard, P. Ciceri, L. M. Wodicka, G. Pallares, M. Hocker, D. K. Treiber, and P. P. Zarrinkar · 2011
Earlier work this paper cites.
Navigating the kinome
J. T. Metz, E. F. Johnson, N. B. Soni, P. J. Merta, L. Kifle, and P. J. Hajduk · 2011
Earlier work this paper cites.
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
G. J. van Westen, J. K. Wegner, A. P. IJzerman, H. W. van Vlijmen, and A. Bender · 2011
Earlier work this paper cites.
Propy: A tool to generate various modes of chou’s pseaac
D.-S. Cao, Q.-S. Xu, and Y.-Z. Liang · 2013
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A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition
A. Sharma, J. Lyons, A. Dehzangi, and K. K. Paliwal · 2013
Earlier work this paper cites.
Reliable estimation of prediction errors for qsar models under model uncertainty using double cross-validation
D. Baumann and K. Baumann · 2014
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Multi-task neural networks for qsar predictions
G. E. Dahl, N. Jaitly, and R. Salakhutdinov · 2014
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Similarity-based machine learning methods for predicting drug–target interactions: A brief review
H. Ding, I. Takigawa, H. Mamitsuka, and S. Zhu · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Toward more realistic drug–target interaction predictions
T. Pahikkala, A. Airola, S. Pietilä, S. Shakyawar, A. Szwajda, J. Tang, and T. Aittokallio · 2014
Earlier work this paper cites.
Making sense of large-scale kinase inhibitor bioactivity data sets: A comparative and integrative analysis
J. Tang, A. Szwajda, S. Shakyawar, T. Xu, P. Hintsanen, K. Wennerberg, and T. Aittokallio · 2014
Earlier work this paper cites.
Multi-task deep networks for drug target prediction
T. Unterthiner, A. Mayr, G. Klambauer, M. Steijaert, J. K. Wegner, H. Ceulemans, and S. Hochreiter · 2014
Earlier work this paper cites.
Polypharmacology modelling using proteochemometrics (pcm): recent methodological developments, applications to target families, and future prospects
I. Cortés-Ciriano, Q. U. Ain, V. Subramanian, E. B. Lenselink, O. Méndez-Lucio, A. P. IJzerman, G. Wohlfahrt, P. Prusis, T. E. Malliavin, G. J. van Westen, and A. Bender · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Predicting hepatotoxicity using toxcast in vitro bioactivity and chemical structure
J. Liu, K. Mansouri, R. S. Judson, M. T. Martin, H. Hong, M. Chen, X. Xu, R. S. Thomas, and I. Shah · 2015
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Deep neural nets as a method for quantitative structure–activity relationships
J. Ma, R. P. Sheridan, A. Liaw, G. E. Dahl, and V. Svetnik · 2015
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Androgen receptors beyond prostate cancer: an old marker as a new target
J. Munoz, J. J. Wheler, and R. Kurzrock · 2015
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Chapter 7 - validation of QSAR models
K. Roy, S. Kar, and R. N. Das · 2015
Low data drug discovery with one-shot learning
H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande · 2017
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Atomic convolutional networks for predicting protein-ligand binding affinity
J. Gomes, B. Ramsundar, E. N. Feinberg, and V. S. Pande · 2017
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Simboost: A read-across approach for predicting drug–target binding affinities using gradient boosting machines
T. He, M. Heidemeyer, F. Ban, A. Cherkasov, and M. Ester · 2017
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Beyond the hype: deep neural networks outperform established methods using a chembl bioactivity benchmark set
E. B. Lenselink, N. Ten Dijke, B. Bongers, G. Papadatos, H. W. Van Vlijmen, W. Kowalczyk, A. P. IJzerman, and G. J. Van Westen · 2017
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Targeting the androgen receptor in triple-negative breast cancer: Current perspectives
A. Mina, R. Yoder, and P. Sharma · 2017
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Deep learning in neural networks: An overview
J. Schmidhuber · 2015
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R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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I. Wallach, M. Dzamba, and A. Heifets · 2015
Cited alongside, same era.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Molecular graph convolutions: Moving beyond fingerprints
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley · 2016
Cited alongside, same era.
Cerapp: Collaborative estrogen receptor activity prediction project
K. Mansouri, A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. M. Richard, and C. M. Grulke · 2016
Cited alongside, same era.
Ani-1: An extensible neural network potential with dft accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2017
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Deep-learning-based drug–target interaction prediction
M. Wen, Z. Zhang, S. Niu, H. Sha, R. Yang, Y. Yun, and H. Lu · 2017
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深度学习在化学信息学中的应用(the application of deep learning in cheminformatics)
Y. 徐优俊 Xu and J. 裴剑锋 Pei · 2017
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In silico identification of protein targets for chemical neurotoxins using toxcast in vitro data and read-across within the qsar toolbox
Y. Chushak, H. Shows, J. Gearhart, and H. Pangburn · 2018
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Pubchem 2019 update: improved access to chemical data
S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, and E. E. Bolton · 2018
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Large-scale comparison of machine learning methods for drug target prediction on chembl
A. Mayr, G. Klambauer, T. Unterthiner, M. Steijaert, J. K. Wegner, H. Ceulemans, D.-A. Clevert, and S. Hochreiter · 2018
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DeepDTA: Deep drug-target binding affinity prediction
H. Öztürk, E. Ozkirimli, and A. Özgür · 2018
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Summary files from invitrodb_v2
US, EPA · 2018
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Toxicity forecaster (toxcast) fact sheet
US, EPA · 2018
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Moleculenet: A benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
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Developmental therapeutics program, division of cancer treatment and diagnosis
National Cancer Institute · 2019
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Deep Learning for the Life Sciences
B. Ramsundar, P. Eastman, K. Leswing, P. Walters, and V. Pande · 2019
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