We will screen out the hit compounds using a Structural Systems Pharmacology (SSP) scheme (1-2). In this scheme, the core is the function-site interaction fingerprint (Fs-IFP) approach (3). Using the Fs-IFP approach, we explore the structural insights into the binding sites across the whole structural proteome. Additionally, this SSP scheme combines MD simulations, Free energy calculations, and machine learning models. Specifically, we first explore the characteristics of the binding site of SARS-CoV-2 helicase NSP13  based on the whole structural proteome using the Fs-IFP method. Then, upon the insights into the binding site we obtained above, we screen the compound datasets virtually using the off-the-shell, classical high-throughput docking tools, such as Autodock Vina. 3) Using MD simulations, the flexibility of the binding sites will be revealed and the corresponding docking process will be performed to target the different conformations. 4) The compounds with top scores will be further optimized by checking the binding affinity and the binding fingerprints.  (1) Zheng Zhao, Philip E. Bourne. J. Proteome Res. 2020, 19 (11), 4698–4705 (2) Zheng Zhao, Philip E. Bourne.  J. Chem. Theory Comput. 2020 16 (5), 3152-3161 (3) Zheng Zhao, Li Xie, Lei Xie, Philip E. Bourne. J. Med. Chem. 2016 59 (9), 4326-4341

IChem (from Dr. Rognan group)

Autodock Vina (Docking)

Acemd (MD simulations)