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Exploring a Siamese Neural Network Architecture for One-Shot Drug Discovery

The application of deep neural networks in drug discovery is mainly due to their enormous potential to significantly increase the predictive power when inferring the properties and activities of small-molecules. However, in the traditional drug discovery process, where supervised data is scarce, the lead-optimization step is a low-data problem, making it difficult to find molecules with the desired therapeutic activity and obtain accurate predictions for candidate compounds. One major requirement to ensure the validity of the obtained neural network models is the need for a large number of training examples per class, which is not always feasible in drug discovery applications. This invalidates the use of instances whose classes were not considered in the training phase or in data where the number of classes is high and oscillates dynamically. The main objective of the study is to optimize the discovery of novel compounds based on a reduced set of candidate drugs. We propose a Siamese neural network architecture for one-shot classification, based on Convolutional Neural Networks (CNNs), that learns from a similarity score between two input molecules according to a given similarity function. Using a one-shot learning strategy, few instances per class are needed for training, and a small amount of data and computational resources are required to build an accurate model. The results achieved demonstrate that using a Siamese Deep Neural Network for one-shot classification leads to overall improved performance when compared to other state-of the-art models. The proposed architecture provides an accurate and reliable prediction of novel compounds considering the lack of biological data available for drug discovery tasks.