This study introduces NP-VAE, a deep-learning method based on variational autoencoders, designed to manage large molecular structures, including natural compounds with chirality. NP-VAE constructs a chemical latent space from large compound datasets, achieving higher reconstruction accuracy and stable generative model performance. Exploration of this latent space facilitated comprehensive analysis of a compound library and generation of novel structures with optimized functions.
