Virtual Screening of Antitumor Inhibitors Targeting BRD4 Based on Machine Learning Methods

BRD4 is a hot antitumor target. In this study, three kinds of machine learning methods were used to establish classification models of BRD4 inhibitors, achieving satisfactory prediction performance. Through comparison, random forest model worked best, the parameters of which were also optimized. Then, the best random forest model was applied to perform virtual screening against ZINC database and a total of 89 potential compounds with BRD4 inhibitory activity were eventually identified. Further, seven molecules were chosen from the hits, and a docking calculation was carried out for each molecule, showing a strong interaction between ligand and BRD4. Subsequently, these molecules were evaluated by molecular dynamics simulations, all having certain binding stability. The results have proved the effectiveness of the developed models based on machine learning methods and the molecules filtered by virtual screening. These models not only can guide the practice for the molecular design and synthesis, but also can provide great possibility for the discoveries and final approvals of anti-cancer drugs targeting BRD4.

Automated summary

The machine learning models established for BRD4 inhibitors showed good predictive performance and identified potential compounds through virtual screening. The molecules selected from the hits demonstrated strong interaction with BRD4 in docking calculations and exhibited binding stability in molecular dynamics simulations.