3.2 Quality of Article Assessment Study and Summary of Studies

We have summarized the quality of articles in our studies in Table 1. Bias in computational drug research studies is not well established, as stated in the methods. But one potential bias is the target's molecular plasticity. The target's flexibility is usually limited or ignored when only molecular docking is used in a study. In this case, MD simulation could play a key role in drug discovery and design, as well as pre-and post-docking simulation. It can be used as a generator for multiple target conformations for virtual screening, or as a validator for post-docking to distinguish between improper docking poses and meaningful ones (15). 13 of our reviewed papers have been validating further by using various molecular dynamics programmes such as GROMACS (16), UNRES (17), NAMD (18), Schrodinger Desmond (19), and AMBER (20).

Table 1. Quality of articles included in systematic review.

The majority of the reviewed articles use GROMACS and Schrodinger Desmond as their methods of validation and use various configurations for the simulation. 9 articles used a single target as their receptors, which is either ACE2 or the SARS-CoV-2 Spike Protein (SP), while 6 of the articles use a single target but with addition other than ACE2 or the SARS-CoV-2 SP. One article used exactly double targets which are ACE2 and SARS-CoV-2 SP, and the last 5 use both targets with the addition of another target. These other targets are proteins which also contribute to the infection or multiplication process of the virus such as Main protease (Mpro)/3-chymotrypsin-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), and other non-structural protein (Nsp) for example Nsp-3 and Nsp-9. Crystal resolution is also one of the crucial parameters in molecular docking and dynamics studies. In this review, all the crystal structures used in the studies range from 2 Å to 4 Å, considered adequate.

Resolution is defined as a metric for assessing the quality of data collected on the protein or nucleic acid crystal. It evaluates the level of detail in the diffraction pattern as well as the level of detail that will be visible when the electron density map is computed (42). Higher resolution values, such as 1Å, are highly ordered and allow you to see every atom in the electron density map, whereas lower resolution values, such as 3Å or higher, only show the basic contours of the protein chain (43,44).

The docking tools mainly used in the reviewed literature are Autodock4 (45) and Autodock Vina (46), though other tools are being used such as GLIDE (47), Cresset Flare (48), or MOE (49). The structure of the database mainly comes from PubChem (50), but other databases such as DrugBank (51), Ambinter (52), and ChemDiv (53) are also being used.

Table 2 provides a comprehensive summary of the included studies, highlighting the methods, protocols, software, ligand candidates, targets, grid sizes in virtual screening and molecular docking, ADMET/physicochemical analysis software, and key interacting residues. Moreover, the table also delineates the important residues implicated in the molecular interactions under investigation, providing valuable insights into the specific amino acids or functional groups involved in driving the observed effects. This information serves as a valuable resource for understanding the research parameters and findings. In subsequent subsections, we discuss each parameter in detail, offering insights into the methodologies employed and their implications. This comprehensive analysis aims to provide a deeper understanding of the reviewed studies, shedding light on the approaches utilized and their impact on the field of research.

3.3 Virtual Screening

There are millions of chemical 'libraries' that a trained chemist could hope to synthesise. Combinatorial chemists have already demonstrated in several prototype systems that libraries containing 1,000-100,000 compounds can be assembled (54). Virtual screening help chemist decides what compound should be synthesised (55). Virtual screening can be done by docking method or pharmacophore method.

Pharmacophores are the "refined" essence of what makes an effective ligand-receptor interaction, explicitly three-dimensional, and represent fundamental physicochemical aspects of ligand-receptor interactions, and are extremely useful when experimental structural data is unavailable and homology models are unreliable. In that case, a good pharmacophore model could give powerful insight and screen more effectively (56,57). Since the conformer is only compared against a three-point pharmacophore model, the method of virtual screening using pharmacophore could be very useful for a large number of compounds when compared to virtual screening using the docking method (54). This is due to the memory used for each conformation is not as large relative to when docking is required.

This method of virtual screening using pharmacophore could be seen in the work of Pokhrel et al. (37) which screened more than 11 thousand from the Ambinter database. The complicated steps in the pharmacophore model method are in the step of model validation. The literature used the GH scoring method for pharmacophore model validation and also includes enrichment factor and goodness of hit score. This validation needs to use a set of other databases called decoy compounds, which are usually available in the Database of Useful Decoys (DUDe) (58). This database has to be compared to active compounds in this matter they use known active ACE2 inhibitors from CheMBL (59) and a literature search. After the model is validated, then it can be used as the parameter for the screening. This could be a problem when there is no available decoy database, or the number of active compounds is inadequate.

Table 2. Summary of studies included in the systematic review.

3.4 Molecular Docking

The docking method is another option for virtual screening. It can also further be used to validate virtual screening results. Molecular docking studies are mainly used to predict the ligand-receptor complex's binding affinity, preferred binding pose, and interaction with the least amount of free energy. Docking studies also can reveal the interaction between protein-ligand, protein-nucleotide, and also protein-protein interactions (PPIs). Noncovalent interactions can include ionic bonds, hydrogen bonds, and van der Waals interactions (60). In addition to the software mentioned previously in the summary of studies, several other software options are widely used in many molecular docking studies. RosettaLigand (61), Surflex (62), and Ligandfit (63) are some of the other popular software.

The docking mechanism is a two-step mechanism. It started with sampling conformation of the ligand in the receptor then followed by ranking these conformations using a scoring function. The effectiveness of a docking programme is determined by two major factors: search algorithms and scoring functions (64).

There are 2 main algorithms in molecular docking, which is the stochastic algorithm when where the search is carried out by modifying the ligand conformation or population of ligands. Example algorithms for this method are Monte Carlo and Genetic Algorithms (65). On the other hand, systematic search methods promote minor modifications in structural parameters, which gradually change the conformation of the ligands. The algorithm examines the energy landscape of the conformational space and, after many search and evaluation cycles, converges on the lowest energy solution corresponding to the most likely binding mode. The systematic algorithms are presented in GLIDE and DOCK (66).

Scoring functions are used to predict the target-ligand complex's binding free energy, which is a measure of the small molecule's binding potency for the biomolecular target. Scoring functions are classified into three types: force-field-based scoring, empirical-based scoring, and knowledge-based scoring (67). AutoDock scoring is an example of force-field-based scoring, which is derived from the classic force field and evaluates the binding energy as a sum of nonbonded interactions. Empirical-based scoring, such as GlideScore, is a weighted sum of various types of receptor-ligand interactions. DrugScore is a knowledge-based scoring system that penalises repulsive interactions while favouring preferred contact between each of the atoms in the protein and ligand within a given cut-off (64).

3.5 Grid Size and Parameter

In our reviewed studies, we examined several variations in the grid size parameter. Several studies reported the grid size qualitatively, stating that they used active site residues or critical interacting residues as grid size (22,24,35,38). Because of the lack of quantitative data, this approach of disclosures would, of course, reduce the reproducibility of the studies. In other studies, the grid size is determined differently for each receptor target (33). In others, it is the same size for all tested receptors (23,25–27,39–41). Understandably, a specific PDB will have a set of specific grid sizes that can be used in it, but the grid size should always be bigger than the ligand that is docked. According to Feinstein and Brylinski's research, the highest accuracy is obtained when the dimensions of the search space are 2.9 times larger than the radius of the gyration of a docking compound (68). They developed a procedure based on this discovery to customise the box size for individual query ligands to maximise docking accuracy. This finding essentially reduces the number of scoring failures caused by overly generous box sizes while also avoiding sampling failures caused by a too-narrow search space.

3.6 Database of Chemicals

Although the majority of the chemical structure is obtained from PubChem, the dataset used for each study is unique to each author. Several studies are being conducted to investigate whether already approved drugs can be repurposed from their original purpose to become ACE2 blockers and SARS-CoV-2 inhibitors (31,35,36). Another method for preparing the dataset for testing is to look for compounds found in traditional medicine or plant constituents as drug candidates (22,24,39,40,25,27–30,33,34,38). The final notable approach is to select a chemical suitable as a candidate from a large dataset ranging from 10,000 to 500,000 compounds (32,37,41). However, these differences in approach may provide beneficial insight from different perspectives, bringing us closer to real drug candidates ready for development.

3.7 Target Receptors

We may find a range of PDB IDs for ACE2 and SARS-CoV-2 Spike Protein receptors in this review study. The same PDB ID was used in several articles for the same receptors. We can see this in four publications (24,25,32,33) for ACE2, which used PDB 1R42 (69) as the receptors. Some other interesting point is that a PDB file can be used as a receptor target for ACE2 or SARS-CoV-2 because it contains both receptors in one file. We can see the PDB 6M0J (70) can be used as a SARS-CoV-2 Spike Protein target in 5 articles