Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening.

ABSTRACT: Indoleamine 2,3-dioxygenase (IDO), an immune checkpoint, is a promising target for cancer immunotherapy. However, current IDO inhibitors are not approved for clinical use yet; therefore, new IDO inhibitors are still demanded. To identify new IDO inhibitors, we have built naive Bayesian (NB) and recursive partitioning (RP) models from a library of known IDO inhibitors derived from recent publications. Thirteen molecular fingerprints were used as descriptors for the models to predict IDO inhibitors. An in-house compound library was virtually screened using the best machine learning model, which resulted in 50 hits for further enzyme-based IDO inhibitory assays. Consequently, we identified three new IDO inhibitors with IC₅₀ values of 1.30, 4.10, and 4.68 μM. These active compounds

SUBMITTER: Zhang H 

PROVIDER: S-EPMC6071933 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature