Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity.

ABSTRACT: The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity o

SUBMITTER: Ochiai T 

PROVIDER: S-EPMC10654724 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature