On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.

ABSTRACT: A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compoun

SUBMITTER: Lim S 

PROVIDER: S-EPMC9399946 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature