Variational Autoencoders

This study examines three unsupervised dimensionality reduction techniques (PCA, UMAP, and VAEs) for toxicology classification tasks. The research compares these embedding methods against standard molecular fingerprint models and explores transfer learning by training embedders on external chemical compound datasets. By testing various embedding dimensions and external dataset sizes, the findings demonstrate that UMAP can effectively complement established techniques like PCA and VAE for pre-compression in toxicology. However, VAE’s generative approach shows superior performance in pre-compression for classification accuracy.