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Interleukin-1 receptor associated kinase-1 (IRAK1) exhibits important roles in inflammation, infection, and autoimmune diseases; however, only a few inhibitors have been discovered. In this study, at first, a discriminatory structure-based virtual screening (SBVS) was employed, but only one active compound (compound  1 , IC 50  = 2.25 μM) was identified. The low hit rate (2.63%) which derives from the weak discriminatory power of docking among high-scored molecules was observed in our virtual screening (VS) process for IRAK1 inhibitor. Furthermore, an artificial intelligence (AI) method, which employed a support vector machine (SVM) model, integrated information of molecular docking, pharmacophore scoring and molecular descriptors was constructed to enhance the traditional IRAK1-VS protocol. Using AI, it was found that VS of IRAK1 inhibitors excluded by over 50% of the inactive compounds, which could significantly improve the prediction accuracy of the SBVS model. Moreover, four active molecules (two of which exhibited comparative IC 50  with compound  1 ) were accurately identified from a set of highly similar candidates. Amongst, compounds with better activity exhibited good selectivity against IRAK4. The AI assisted workflow could serve as an effective tool for enhancement of SBVS.

frontiers in oncology

Evaluation of Artificial Intelligence in Participating Structure-Based Virtual Screening for Identifying Novel Interleukin-1 Receptor Associated Kinase-1 Inhibitors