461815 Discriminative Neural Embeddings of Latent Variable Models for Molecular Property Prediction
We propose an effective and scalable approach for structured data representation which is based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces with ultimate regressor/classifier using discriminative information. The algorithm runs a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. This can be implemented as a recurrent neural network, where the parameters for the feature spaces and classifiers are trained in an end to end fashion. We deployed our algorithm on several computational chemistry problems, including the compound and protein classification tasks. We achieved state-of-the-art results on several commonly used benchmark datasets, including NCI1, NCI90, ENZYMES and D&D. We also applied our algorithm on Harvard Clean Energy Project dataset with millions of molecules and predicted power conversion efficiency and energy. We achieved below 0.1 mean absolute error, while using significantly small number of parameters than alternatives.
See more of this Group/Topical: Computational Molecular Science and Engineering Forum