Combining Molecular Docking and Graph Convolutional Network to Discover Potential Selective Limk1 Inhibitors

Abstract The LIM kinase 1 (Limk1) has been demonstrated to be considered a therapeutic target and selective inhibitors of Limk1 rather Rho-associated kinase 2 (ROCK2) are considered of interest for the treatment of several indications such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and cancer migration/invasion. Here, we utilized molecular docking to screen potential compounds of Limk1 from Traditional Chinese Medicine (TCM) database. Meanwhile, we performed a three-dimensional graph convolutional network (3DGCN), based on 3D molecular graph, to predict the inhibitory activity of Limk1 and ROCK2. Compared with the baseline models (RF, GCN and Weave), the 3DGCN achieved higher accuracy and the averaged RMSE values on test sets for Limk1 and ROCK2 were 0.721 and 0.852 respectively. In 3DGCN, above 80% of the test-set molecules from both two datasets were predicted within absolute error of 1.0 and the feature visualization suggested that it could automatically learn relevant structure features including 3D molecular information from a specific task for prediction. Furthermore, molecular dynamics (MD) simulations within 100 ns were employed to verify the stability of ligand-protein complexes and reveal the binding modes of the potential selective lead compounds of Limk1. Finally, integrating docking results, the predicted values by the 3DGCN and the MD analysis, we found that 7549, 2007_15649 and 3519 might be the potential and selective inhibitors for Limk1 receptor rather than ROCK2.