Using Deep Siamese Neural Networks to Speed up Natural Products Research

Natural products (NPs) are an important source of novel disease treatments. A bottleneck in the search for new NPs is structure determination of molecules extracted from biological organisms. One method is to use 2D Nuclear Magnetic Resonance (NMR) spectroscopy, which indicates bonds between nuclei in the compound and hence is the “fingerprint” of the compound. Computing a similarity score between 2D NMR spectra for a novel compound and a compound whose structure is known provides clues to the structure of the novel compound. Standard approaches to this problem do not scale to larger databases of compounds. Here we use deep convolutional Siamese networks to map NMR spectra to a cluster space, where similarity is given by the distance in the space. This approach results in an AUC score that is more than four times better than an approach using LDA.