Discovery of novel potential 11β-HSD1 inhibitors through combining deep learning, molecular modeling, and bio-evaluation.

Abstract

11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) has been shown to play an important role in the treatment of impaired glucose tolerance, insulin resistance, dyslipidemia, and obesity and is a promising drug target. In this study, we built a gated recurrent unit (GRU)-based recurrent neural network using 1,854,484 (processed) drug-like molecules from ChEMBL and the US patent database and successfully built a molecular generative model of 11βHSD1 inhibitors by using the known 11β-HSD1 inhibitors that have undergone transfer learning, our constructed GRU model was able to accurately capture drug-like molecules evaluated using traditional machine model-related syntax, and transfer learning can also easily generate potential 11β-HSD1 inhibitors. By combining Lipinski's and absorption, distribution, metabolism, excretion, and toxicity (ADME/T) analyses to filter nonconforming molecules and stepwise screening through molecular docking and molecular dynamics simulation, we finally obtained 5 potential compounds. We found that compound 02 is identical to a previously published inhibitor of 11β-HSD1. We selected compounds 02 and 05 with the lowest binding free energy for in vitro activity validation and found that compound 02 possessed inhibitory activity but was not as potent as the control. In conclusion, our study provides new ideas and methods for the development of new drugs and the discovery of new 11β-HSD1 inhibitors.