01/Molecular Docking Based Virtual Screening
Molecular Docking Based Virtual Screening
Molecular docking is a drug screening approach based on the three-dimensional structure of target proteins.
By docking small-molecule compounds with target proteins, both scoring functions and spatial conformations are comprehensively analyzed, including electrostatic interactions, hydrogen bonding, hydrophobic interactions, and van der Waals forces.
This approach enables the exploration of the binding modes and conformations between small-molecule ligands and macromolecular receptors, helping to explain the origin of compound activity and providing guidance for rational structure optimization.
With the support of binding affinity scoring functions, the interaction strength between small molecules and target proteins can be quantitatively evaluated, offering a reliable basis for candidate selection and the identification of potentially active compounds for experimental validation.
Virtual screening based on molecular docking is one of the most promising tools in modern drug discovery. As this method places minimal restrictions on compound structures, it holds great potential for identifying structurally novel lead compounds.
02/Pharmacophore Based Virtual Screening
Pharmacophore Based Virtual Screening
Pharmacophore modeling is an efficient drug screening approach based on the key features of small-molecule compounds.
By analyzing the pharmacophoric characteristics of one or more active molecules, the essential features responsible for biological activity can be identified. Pharmacophore screening requires relatively low computational cost and can be performed prior to molecular docking. It enables rapid screening of millions to tens of millions of compounds within a short timeframe.
With as few as one or several active compounds provided by the customer, a common pharmacophore model can be constructed to screen for molecules with similar features and guide the design and synthesis of new active compounds.
In addition, some molecular modeling software offers methods to predict the biological activity of matched molecules, such as pharmacophore-based QSAR models.
However, virtual screening based on pharmacophore models often yields compounds with structural properties similar to those used to build the model, which may limit the discovery of novel scaffolds for lead optimization.
In this case, we applied molecular docking techniques to investigate the binding ability of Compound 1with the PARP1 protein.
The DOCK 6.7 program was used to predict the binding modes of Compound 1 within PARP1, retaining up to 20 docking conformations.
The results indicated that multiple docking conformations were present at the binding site, with their corresponding scores shown in Table 1.
Based on the docking scores and binding modes, the second-ranked docking conformation was selected for further analysis of the interaction pattern.
The amide carbonyl oxygen atom on the seven-membered ring of Compound 1 forms hydrogen bond interactions with the amino acid residues Ser904 and His862. Meanwhile, the amide nitrogen atom forms a hydrogen bond interaction with Gly863 at a distance of 3.33 Å. These interactions help anchor the binding orientation of the compound and contribute to electrostatic interactions (Grid_es = -1.574563 kcal/mol).
The two rings of the benzimidazole moiety form parallel π–π stacking interactions with Tyr90, with centroid–centroid distances of 4.21 Å and 4.93 Å, respectively.
In addition, the side-chain phenyl ring forms a T-shaped π–π stacking interaction with Tyr896, with a centroid distance of 5.47 Å.
At the same time, the compound also forms hydrophobic interactions with residues Tyr889, Tyr896, Tyr907, and Glu998. These hydrophobic and π–π stacking interactions provide strong van der Waals contributions (Grid_vdw = -53.78 kcal/mol).
In summary, the interaction between Compound 1 and the protein PARP1 is mainly driven by π–π stacking and hydrophobic interactions, while hydrogen bonds help stabilize and lock the binding orientation.
