Low Data Drug Discovery with One-Shot Learning

ACS Central Science

Volumn 3, Issue 4, 2017, Pages 283-293

  Altae Tran, Han ^a   Ramsundar, Bharath ^b   Pappu, Aneesh S ^b   Pande, Vijay ^c  

^a Massachusetts Institute of Technology Cambridge   (United States)

^b Stanford University   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS; ITERATIVE METHODS; MOLECULES; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; DISTANCE METRICS; DRUG DISCOVERY APPLICATIONS; ITERATIVE REFINEMENT; ONE-SHOT LEARNING; OPEN SOURCE FRAMEWORKS; PREDICTIVE POWER; SMALL MOLECULES;

OUTSOURCING;

EID: 85026486382     PISSN: 23747943     EISSN: 23747951     Source Type: Journal    
DOI: 10.1021/acscentsci.6b00367     Document Type: Article

References (35)

1. Ma, J. et al. J. Chem. Inf. Model. 2015, 55, 263-274
2. Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016
3. Ma, J.; Sheridan, R. P.; Liaw, A.; Dahl, G. E.; Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships J. Chem. Inf. Model. 2015, 55, 263-274 10.1021/ci500747n
4. Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016.
5. Waring, M. J.; Arrowsmith, J.; Leach, A. R.; Leeson, P. D.; Mandrell, S.; Owen, R. M.; Pairaudeau, G.; Pennie, W. D.; Pickett, S. D.; Wang, J.; Wallace, O.; Weir, A. An analysis of the attrition of drug candidates from four major pharmaceutical companies Nat. Rev. Drug Discovery 2015, 14, 475-486 10.1038/nrd4609
6. Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; Fei-Fei, L. ImageNet Large Scale Visual Recognition Challenge Int. J. Comp. Vis (IJCV) 2015, 115, 211-252 10.1007/s11263-015-0816-y
7. Deng, L.; Hinton, G.; Kingsbury, B. New types of deep neural network learning for speech recognition and related applications: An overview Int. Conf. Acous. Speech Signal Proc. 2013, 8599-8603 10.1109/ICASSP.2013.6639344
8. Wu, Y. et al., Googles Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv preprint arXiv:1609.08144, 2016.
9. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search Nature 2016, 529, 484-489 10.1038/nature16961
10. Dahl, G. Deep Learning How I Did It: Merck 1st place interview. http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/, November 1, 2012.
11. Ramsundar, B.; Kearnes, S.; Riley, P.; Webster, D.; Konerding, D.; Pande, V. Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072, 2015.
12. Unterthiner, T.; Mayr, A.; Klambauer, G.; Steijaert, M.; Wegner, J. K.; Ceulemans, H.; Hochreiter, S. Deep Learning as an Opportunity in Virtual Screening. Neural Inf. Proc. Sys. DL Workshop (NIPS DL Workshop) 2014, 27.
13. Lusci, A.; Pollastri, G.; Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules J. Chem. Inf. Model. 2013, 53, 1563-1575 10.1021/ci400187y
14. 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 Neural Inf. Proc. Sys. (NIPS) 2015, 2224-2232
15. Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular Graph Convolutions: Moving Beyond Fingerprints J. Comput.-Aided Mol. Des. 2016, 30, 595-608 10.1007/s10822-016-9938-8
16. Rogers, D.; Hahn, M. Extended-connectivity fingerprints J. Chem. Inf. Model. 2010, 50, 742-754 10.1021/ci100050t
17. Subramanian, G.; Ramsundar, B.; Pande, V.; Denny, R. A. Computational Modeling of β-secretase 1 (BACE-1) Inhibitors using Ligand Based Approaches J. Chem. Inf. Model. 2016, 56, 1936-1949 10.1021/acs.jcim.6b00290
18. Lake, B. M.; Salakhutdinov, R.; Tenenbaum, J. B. Human-level concept learning through probabilistic program induction Science 2015, 350, 1332-1338 10.1126/science.aab3050
19. Santoro, A.; Bartunov, S.; Botvinick, M.; Wierstra, D.; Lillicrap, T. One-shot Learning with Memory-Augmented Neural Networks. arXiv preprint arXiv:1605.06065, 2016.
20. Vinyals, O.; Blundell, C.; Lillicrap, T.; Kavukcuoglu, K.; Wierstra, D. Matching Net- works for One Shot Learning. Advances in Neural Information Processing Systems, 2016, pp 3630-3638.
21. He, K.; Zhang, X.; Ren, S.; Sun, J. Identity Mappings in Deep Residual Networks. European Conference on Computer Vision, 2016, pp 630-645.
22. Abadi, M. et al., TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). Savannah, Georgia, USA, 2016, pp 265-283.
23. Koch, G. Siamese neural networks for one-shot image recognition. Ph.D. thesis, University of Toronto, 2015.
24. Hochreiter, S.; Schmidhuber, J. Long short-term memory Neural Comp 1997, 9, 1735-1780 10.1162/neco.1997.9.8.1735
25. Graves, A.; Jaitly, N.; Mohamed, A.-r. Hybrid speech recognition with deep bidirec- tional LSTM Aut. Speech Rec. Und. (ASRU) 2013, 273-278 10.1109/ASRU.2013.6707742
26. Vinyals, O.; Bengio, S.; Kudlur, M. Order matters: Sequence to sequence for sets. arXiv preprint arXiv:1511.06391, 2015.
27. Convolutional Neural Networks. http://cs231n.github.io/convolutional-networks/, Accessed: 2016-11-06.
28. Gómez-Bombarelli, R.; Duvenaud, D.; Hernández-Lobato, J. M.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design using a Data-Driven Continuous Representation of Molecules. rXiv preprint arXiv:1610.02415, 2016.
29. Kingma, D.; Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980, 2014.
30. Tox21 Challenge. https://tripod.nih.gov/tox21/challenge/, Accessed: 2016-11- 06.
31. Unterthiner, T.; Mayr, A.; Klambauer, G.; Hochreiter, S. Toxicity prediction using deep learning. arXiv preprint arXiv:1503.01445, 2015.
32. Kuhn, M.; Letunic, I.; Jensen, L. J.; Bork, P. The SIDER database of drugs and side effects Nucleic Acids Res. 2016, 44 (D1) 1075-1079 10.1093/nar/gkv1075
33. Medical Dictionary for Regulatory Activities. http://www.meddra.org/, Accessed: 2016, -09-20.
34. Rohrer, S. G.; Baumann, K. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data J. Chem. Inf. Model. 2009, 49, 169-184 10.1021/ci8002649
35. RDKit, https://github.com/rdkit/rdkit, 2016.