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. 2017 Apr 26;3(4):283-293.
doi: 10.1021/acscentsci.6b00367. Epub 2017 Apr 3.

Low Data Drug Discovery with One-Shot Learning

Han Altae-Tran  1 , Bharath Ramsundar  2 , Aneesh S Pappu  2 , Vijay Pande  2
Affiliations

Low Data Drug Discovery with One-Shot Learning

Han Altae-Tran et al. ACS Cent Sci. .

Abstract

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf.

Model: 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Schematic of Network Architecture for one-shot learning in drug discovery.
Figure 2
Figure 2
Pictorial depiction of iterative refinement of embeddings. Inputs/outputs are two-dimensional for illustrative purposes, with q1 and q2 forming the coordinate axes. Red and blue points depict positive/negative samples (for illustrative purposes only). The original embedding g′(S) is shown as squares. The expected features r are shown as empty circles.
Figure 3
Figure 3
Graphical representation of the major graph operations described in this paper. For each of the operations, the nodes being operated on are shown in blue, with unchanged nodes shown in light blue. For graph convolution and graph pool, the operation is shown for a single node, v; however, these operations are performed on all nodes v in the graph simultaneously.
See this image and copyright information in PMC

References

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