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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 IC 50 values of 1.30, 4.10, and 4.68 μM. These active compounds also showed IDO inhibitory activities in cell-based assays. The compounds belong to the tanshinone family, a typical scaffold family derived from Danshen (a Chinese herb), the dried root of Salvia miltiorrhiza , which has been widely used in China, Japan, the United States, and other European countries for the treatment of cardiovascular and cerebrovascular diseases. Thus, we discovered a new use for Danshen using machine learning methods. Surface plasmon resonance (SPR) experiments proved that the inhibitors interacted with the IDO target. Molecular dynamic simulations demonstrated the binding modes of the IDO inhibitors.

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