Fig. 1
From: Deep learning pipeline for accelerating virtual screening in drug discovery
Graphical synopsis illustrates the workflow of the VirtuDockDL pipeline for virtual screening in drug discovery. It begins with identifying active and inactive molecules for a target protein. De-novo molecules are generated, filtered by drug-likeness rules, and their features are selected based on graph-based features, molecular descriptors, and fingerprints. GNN model is trained and evaluated using metrics like ROC curves. The best model is used to screen a compound library for potential inhibitors. Protein structures are prepared and refined for molecular docking simulations. The results are visualized and benchmarked against experimental data. The VirtuDockDL platform provides a user interface to manage all these steps efficiently.
