2017

SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules

Bjerrum, Esben Jannik

Understand

Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule.

One molecule can however have multiple SMILES strings, which is a reason that canonical SMILES have been defined, which ensures a one to one correspondence between SMILES string and molecule.
Here the fact that multiple SMILES represent the same molecule is explored as a technique for data augmentation of a molecular QSAR dataset modeled by a long short term memory (LSTM) cell based neural network.
The augmented dataset was 130 times bigger than the original.

Built on

doi:10.1021/ci034143r
J. J. Sutherland, L. A. O’Brien, D. F. Weaver, Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships., Journal of chemical information and computer sciences 43 (2003) 1906–1915 · 1915
Earlier work this paper cites.
D. Weininger, Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules, in: Proc. Edinburgh Math. SOC, Vol. 17, 1970, pp. 1–14
1970
Earlier work this paper cites.
R. E. Carhart, D. H. Smith, R. Venkataraghavan, Atom pairs as molecular features in structure-activity studies: definition and applications, Journal of Chemical Information and Computer Sciences 25 (2) (1985) 64–73
1985
Earlier work this paper cites.
R. Nilakantan, N. Bauman, J. S. Dixon, R. Venkataraghavan, Topological torsion: a new molecular descriptor for sar applications. comparison with other descriptors, Journal of Chemical Information and Computer Sciences 27 (2) (1987) 82–85
1987
Earlier work this paper cites.
P. Y. Simard, D. Steinkraus, J. C. Platt, et al., Best practices for convolutional neural networks applied to visual document analysis., in: ICDAR, Vol. 3, Citeseer, 2003, pp. 958–962
2003
Earlier work this paper cites.
Ctfile formats , http://accelrys.com/products/-informatics/cheminformatics/ctfile-formats/no-fee.php (Dec 2011). URL http://accelrys.com/products/informatics/cheminformatics/ctfile-formats/no-fee.php
2011
Earlier work this paper cites.
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, E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.

Similar

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, IEEE Signal Processing Magazine 29 (6) (2012) 82–97
2012
Cited alongside, same era.
doi:10.1186/1758-2946-4-22
N. M. O’Boyle, Towards a universal smiles representation - a standard method to generate canonical smiles based on the inchi., Journal of cheminformatics 4 (2012) 22 · 2012
Cited alongside, same era.
A. Worachartcheewan, P. Mandi, V. Prachayasittikul, A. P. Toropova, A. A. Toropov, C. Nantasenamat, Large-scale qsar study of aromatase inhibitors using smiles-based descriptors, Chemometrics and Intelligent Laboratory Systems 138 (2014) 120–126
2014
Cited alongside, same era.
A. P. Toropova, A. A. Toropov, Coral software: prediction of carcinogenicity of drugs by means of the monte carlo method, European Journal of Pharmaceutical Sciences 52 (2014) 21–25
2014
Cited alongside, same era.
doi:10.1109/TASLP.2015.2438544
X. Cui, V. Goel, B. Kingsbury, Data augmentation for deep neural network acoustic modeling , IEEE/ACM Trans. Audio, Speech and Lang. Proc. 23 (9) (2015) 1469–1477 · 2015
Cited alongside, same era.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks 61 (2015) 85–117
2015
Cited alongside, same era.
F. Chollet, keras, https://github.com/fchollet/keras (2015)
2015
Cited alongside, same era.
K. Chatfield, K. Simonyan, A. Vedaldi, A. Zisserman, Return of the devil in the details: Delving deep into convolutional nets, arXiv preprint arXiv:1405.3531
Original
Cited in the paper.
R. Gómez-Bombarelli, D. Duvenaud, J. M. Hernández-Lobato, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, A. Aspuru-Guzik, Automatic chemical design using a data-driven continuous representation of molecules, arXiv preprint arXiv:1610.02415
Original
Cited in the paper.
D. Tang, B. Qin, X. Feng, T. Liu, Target-dependent sentiment classification with long short term memory, CoRR, abs/1512.01100
Original
Cited in the paper.
R. Al-Rfou, G. Alain, A. Almahairi, C. Angermueller, D. Bahdanau, N. Ballas, F. Bastien, J. Bayer, A. Belikov, A. Belopolsky, Y. Bengio, A. Bergeron, J. Bergstra, V. Bisson, J. Bleecher Snyder, N. Bouchard, N. Boulanger-Lewandowski, X. Bouthillier, A. de Brébisson, O. Breuleux, P.-L. Carrier, K. Cho, J. Chorowski, P. Christiano, T. Cooijmans, M.-A. Côté, M. Côté, A. Courville, Y. N. Dauphin, O. Delalleau, J. Demouth, G. Desjardins, S. Dieleman, L. Dinh, M. Ducoffe, V. Dumoulin, S. Ebrahimi Kahou, D. Erhan, Z. Fan, O. Firat, M. Germain, X. Glorot, I. Goodfellow, M. Graham, C. Gulcehre, P. Hamel, I. Harlouchet, J.-P. Heng, B. Hidasi, S. Honari, A. Jain, S. Jean, K. Jia, M. Korobov, V. Kulkarni, A. Lamb, P. Lamblin, E. Larsen, C. Laurent, S. Lee, S. Lefrancois, S. Lemieux, N. Léonard, Z. Lin, J. A. Livezey, C. Lorenz, J. Lowin, Q. Ma, P.-A. Manzagol, O. Mastropietro, R. T. McGibbon, R. Memisevic, B. van Merriënboer, V. Michalski, M. Mirza, A. Orlandi, C. Pal, R. Pascanu, M. Pezeshki, C. Raffel, D. Renshaw, M. Rocklin, A. Romero, M. Roth, P. Sadowski, J. Salvatier, F. Savard, J. Schlüter, J. Schulman, G. Schwartz, I. V. Serban, D. Serdyuk, S. Shabanian, E. Simon, S. Spieckermann, S. R. Subramanyam, J. Sygnowski, J. Tanguay, G. van Tulder, J. Turian, S. Urban, P. Vincent, F. Visin, H. de Vries, D. Warde-Farley, D. J. Webb, M. Willson, K. Xu, L. Xue, L. Yao, S. Zhang, Y. Zhang, Theano: A Python framework for fast computation of mathematical expressions , arXiv e-prints abs/1605.02688. URL http://arxiv.org/abs/1605.02688
Original
Cited in the paper.
C. Wang, R. M. Neal, Mcmc methods for gaussian process models using fast approximations for the likelihood, arXiv preprint arXiv:1305.2235
Original
Cited in the paper.

Then

D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, R. P. Adams, Convolutional networks on graphs for learning molecular fingerprints, in: Advances in neural information processing systems, 2015, pp. 2224–2232
2015
Later among the works it cites.
A. Mayr, G. Klambauer, T. Unterthiner, S. Hochreiter, Deeptox: toxicity prediction using deep learning, Frontiers in Environmental Science 3 (2016) 80
2016
Later among the works it cites.
A.-D. Almási, S. Woźniak, V. Cristea, Y. Leblebici, T. Engbersen, Review of advances in neural networks: Neural design technology stack, Neurocomputing 174 (2016) 31–41
2016
Later among the works it cites.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, P. Riley, Molecular graph convolutions: moving beyond fingerprints, Journal of computer-aided molecular design 30 (8) (2016) 595–608
2016
Later among the works it cites.
G. A. Landrum, Rdkit: Open-source cheminformatics software (2016). URL http://www.rdkit.org/,https://github.com/rdkit/rdkit
2016
Later among the works it cites.
T. G. authors, Gpyopt: A bayesian optimization framework in python, http://github.com/SheffieldML/GPyOpt (2016)
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…