Due to the algorithm defect of the database on target prediction, a thorough docking was performed to improve the credibility of systems pharmacology. The crystal structures (.PDB) of selected targets were obtained from RCSB (http://www.rcsb.org/) [48], and the crucial targets were docked with active compounds.

Schrödinger Glide was used to pretreat the 3D protein structure for docking, including adjusting the bond orders to ensure the stability of the chemical bonds between the atoms, adding the missing hydrogen atoms and amino acid residues, optimizing the orientation of amino acids and hydrogen atoms, optimizing the distribution of H-bonds, and removing water molecules and heterogeneous molecules. Finally, energy minimization with a force field OPLS-2005 was supplied.

At the end of pretreatment, the Receptor Grid Generation module was employed to select the ligand-binding cavity and generate a grid in protein. Next, the active compounds were introduced to Maestro and optimized by the liquid simulation of OPLS-2005 all-atoms force field in the LigPrep module, as well as combined into a ligand package. The docking accuracy was evaluated by standard precision (SP), as well as flexible docking. At this point, the docking preparation was completed, the scaling factor and partial charge cutoff of van der Waals radius scaling 1.0 and 0.25 were used to generate the grids on active sites, and ligand package was selected to perform molecular docking in the Ligand Docking module. Next, the generated Glide G-score was used to assess the affinity between compounds and proteins. Moreover, crucial targets were docked with their self-ligands to set positive contrast, and their Glide G-scores were used to measure whether compounds possessed a good affinity to the protein and standardize the score of compounds to be visualized as a heat map by MeV [49] (step 1: normalized genes/rows; step 2: hierarchical clustering: average dot product-complete linkage clustering).

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