An attractive drug target to combat COVID‐19 is the main protease (Mpro) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic‐level resolution, and consequently may provide crucial structural insights for structure‐based drug discovery. In the current study, we report a comparative structural analysis of the Mpro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand‐binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142–Ser144) and residues His164–Glu166 form essential parts of the substrate‐binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate‐binding pocket flexibility and show that two adjacent loops in the Mpro structures (residues Thr45–Met49 and Arg188–Ala191) with high flexibility can regulate the binding cavity’ accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of Mpro. We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top‐ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking.

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6‐D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin‐dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry‐corrected heavy‐atom RMSD and the fraction of recovered ligand‐protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

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It is demonstrated that there is a direct connection between aromaticity and the anisotropy of the π-electron density on planes parallel to the molecular ring. The electron density anisotropy on the plane is measured through the ratio of the in-plane Hessian eigenvalues associated with the eigenvectors lying in the plane. Computations on a wide-ranging set of well-characterized monocyclic systems containing heteroatoms validate the correlation between this one-electron density-based descriptor and aromaticity; in aromatic compounds, the in-plane Hessian eigenvalues are degenerate (or near degenerate) and the anisotropy of the π-electron density is undirected, whereas the results for antiaromatic rings are reversed and the degeneracy of the eigenvalues completely disappears. This finding is in line with our very recent study on [n]annulenes and provides further evidence that the anisotropy of the π-electron density should be considered as a new manifestation of aromaticity.

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The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning algorithms in the development of scoring functions has led to a significant improvement in docking performance. In this work, we introduce a new machine learning (ML) based scoring function called ET-Score, which employs the distanceweighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein À ligand complexes and Extremely Randomized Trees algorithm for the training process. The performance of ET-Score is compared with some successful ML-based scoring functions and several popular classical scoring functions on the PDBbind 2016v core set. It is shown that our ET-Score model (with Pearson's correlation of 0.827 and RMSE of 1.332) achieves very good performance in comparison with most of the ML-based scoring functions and all classical scoring functions despite its extremely low computational cost. ET-Score's codes are freely available on the web at https://github.com/miladrayka/ET_Score.