Low Data Drug Discovery with One-shot Learning
This addresses data scarcity in drug discovery for researchers, though it is incremental as it builds on existing techniques.
The paper tackled the problem of requiring large training data for deep learning in drug discovery by using one-shot learning to lower data needs, resulting in a new architecture that improves distance metric learning for small-molecule predictions.
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. 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 residual LSTM embedding, that, when combined with graph convolutional neural networks, significantly improves the ability to learn 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.