Computer-aided drug discovery of potential semisynthetic inhibitors for SARS-CoV-2 main protease: A multi-phase computational approach

Abstract

Background:

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (M^pro) is a key target for drug development in the fight against COVID-19 and computer-aided drug design (CADD) offers a promising way to discover potential drugs.

Aim:

Building on our team’s previous efforts against COVID-19, we have carefully curated a set of 80 semi-synthetic compounds. These compounds were selected matching the key pharmacophoric features of SARS-CoV-2 M^pro inhibitors.

Objective:

The aim of this study is to identify the most effective inhibitors of SARS-CoV-2 M^pro using a CAAD approach.

Method:

We employed a comprehensive approach, subjecting the selected compounds to molecular docking against M^pro (PDB ID: 6LU7). We then conducted thorough absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluations and toxicity assessments to determine the drug-likeness of the selected compounds. Further substantiation was provided by meticulous density functional theory (DFT) investigations and molecular dynamics (MD) simulations at 150 nanoseconds to study the electronic properties and stability of the complexes formed by the most promising candidates.

Result:

Molecular docking analysis revealed favorable binding modes and robust free energy profiles for compounds 12, 15, 17, 40, 47, 67, and 70. DFT investigations indicated that compounds 12, 15, 67, and 70 have a heightened potential to form efficient binding interactions with M^pro. MD simulations (150 ns) of compounds 67 and 70 showed stable binding within the M^pro active site, with average ligand root mean square deviation (RMSD) values of 7.5 Å (67) and 4.4 Å (70), and a consistent center-of-mass distance (~21.7 Å). Molecular mechanics-generalized born surface area (MM-GBSA) calculations revealed average binding free energies of −25.92 kcal/mol and −30.62 kcal/mol for compounds 67 and 70, respectively, indicating strong and favorable interactions in both cases.

Conclusion:

Our study designates compounds 67 and 70 as promising candidates for effective M^pro inhibition, with strong potential for future experimental studies.

Keywords

CAAD DFT MD simulations molecular docking SARS-CoV-2 main protease semi-synthetic

Introduction

In the past 20 years, computational (in silico) chemistry has become an essential instrument in the design and evaluation of pharmacological agents.¹ This increasing significance arises from several critical developments. The accurate determination of three-dimensional structures for various protein targets in the human body has been accomplished.² Second, notable advancements have occurred in computational software and hardware technologies.³ Third, there has been an enhanced comprehension of structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR).⁴ Consequently, in silico methods have been essential in investigating pharmacokinetic and pharmacodynamic properties, facilitating the identification of correlations between chemical structures and their biological activities. The approaches encompass QSAR modeling,⁵ pharmacophore mapping,⁶ homology modeling,⁷ molecular docking,^8,9 molecular dynamics (MD) simulations,¹⁰ absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling,^11,12 and density functional theory (DFT) calculations.¹³

Almost one-third of the Food and Drug Administration (FDA)-approved drugs are either based on or derived from natural sources in the time range of (1981–2014).¹⁴ Utilizing semi-synthesis on natural products with the goal of producing diverse analogs offers the advantage of uncovering more potent drugs and enabling repurposing effort.¹⁵ Furthermore, such cases facilitate easier and available SAR investigations, presenting a significant opportunity to discover novel bioactive compounds, improve drug-likeness, and enhance pharmacokinetic and pharmacodynamic properties.¹⁶

X-ray structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (M^pro) complexed with an α-ketoamide-type inhibitor were reported by Zhang et al.¹⁷ In addition, four α-ketoamide inhibitors (I, II, III, and IV) were discovered using the X-ray structure as a basis. These complexes provided critical insights into the molecular interactions of the α-ketoamide inhibitors within the active site of the M^pro (Figure 1). The active sites of M^pro are composed of four canonical binding pockets named S1, S1, S2, and S4.¹⁸ The effectiveness of α-ketoamide inhibitors against SARS-CoV-2 M^pro has been well-documented in the literature, primarily due to their covalent, yet reversible interaction in the protease active site. Structurally, α-ketoamides possess an electrophilic keto group adjacent to an amide, which serves as a reactive warhead capable of forming a covalent bonds blocking enzymatic activity.¹⁹ In addition, their peptidomimetic backbone allows for optimal fitting into the S1, S1’, S2, and S4 subsites of M^pro, enhancing both binding specificity and stability.^20–22 Pharmacophoric feature analysis of these inhibitors revealed a consistent pattern across potent M^pro binders, highlighting four key binding moieties—P1, P2, P3, and P4. Each pharmacophoric moiety is believed to play a distinct role by interacting with its corresponding pocket (P1 with S1, P2 with S2, P3 with S4, and P4 with S1’), as illustrated in Figures 1 and 2. This structure-guided pharmacophoric framework served as the foundation for the selection of novel compounds in this study, ensuring that candidate molecules aligned with the essential spatial and functional requirements for effective M^pro inhibition

Figure 1.

The active site of M.^pro

Figure 2.

Chemical structures of α-ketoamide inhibitors I, II, III, and IV. Blue symbols P1, P2, P3, and P4 indicate the binding moieties of the peptidomimetic inhibitor.

To identify potential SARS-CoV-2 inhibitors, we employed a multi-stage computational screening approach against key viral enzymes. Among 310 natural antiviral compounds, the most potential inhibitors against SARS-CoV-2 papain-like protease,²³ M^pro,²³ and nsp10²⁴ were anticipated. Furthermore, between 3009 FDA-approved drugs, several inhibitors against SARS-CoV-2 RNA-Dependent RNA Polymerase,²⁵ papain-like protease,^12,26 M^pro,²⁷ and nsp16-nsp10 2′-o-Methyltransferase Com-plex²⁸ were predicted. Furthermore, the SARS-CoV-2 helicase inhibitors were expected from 5956 traditional Chinese medicine candidates.²⁹ Finally, the most potent semisynthetic and natural inhibitors were selected amid 69 compounds³⁰ and 4924 African natural metabolites,^31,32 respectively.

This study investigates a unique set of 80 semi-synthetic compounds that have not been previously studied as SARS-CoV-2 M^pro inhibitors. While they share key pharmacophoric features with known inhibitors, they represent unexplored chemical space. Our integrative computational approach—combining docking, ADMET analysis, DFT calculations, and MD simulations—adds novelty and depth, enhancing the reliability of identifying promising M^pro-targeting candidates.

Results and discussion

Pharmacophoric features study

A curated set of 80 semi-synthetic compounds (Figure 3), each designed to match the essential pharmacophoric features of known M^pro inhibitors, was sourced from the Eximed Laboratory database³³ for computational screening in this study. The selection was guided by structural similarity to known M^pro inhibitors, particularly the presence of moieties capable of occupying the well-characterized S1, S1’, S2, and S4 binding pockets of the M^pro active site. An initial virtual screening was conducted on these compounds to evaluate their binding affinities toward M^pro using molecular docking techniques. As shown in Figure 4, representative compounds display the essential pharmacophoric elements—corresponding to P1, P2, P3, and P4—that are required for optimal interaction within the enzyme’s active site. This strategic alignment with known binding features provided a rationale for prioritizing these compounds for further computational evaluation.

Figure 3.

The semi-synthetic compound’s chemical structures.

Figure 4.

Samples of the tested compounds have the same essential pharmacophoric features as M^pro inhibitors.

Molecular docking studies

The SARS-CoV-2 M^pro, represented by PDB ID: 6LU7, is a well-established and critical therapeutic target due to its essential role in processing viral polyproteins necessary for viral replication and transcription. Its absence in human proteases makes it highly specific and a safe candidate for antiviral intervention.³⁴ As a result, 6LU7 has been widely employed in numerous structure-based drug design studies and virtual screening efforts aimed at identifying effective M^pro inhibitors.^35–39

Molecular docking studies of 80 semisynthetic compounds were carried against SARS-CoV-2 M^pro (PDB ID: 6LU7) with the co-crystallized ligand (PRD_002214) as a reference (Table 1) to take a look at the binding free energies in addition to the binding modes of the endorsed metabolites.

Table 1.

The calculated ∆G (in Kcal/mol) of the tested semisynthetic compounds and (PRD_002214).

Comp.	∆G	Comp.	∆G	Comp.	∆G
1	−23.7	28	−22.4	55	−23.5
2	−23.8	29	−17.4	56	−22.7
3	−25.4	30	−22.3	57	−21.88
4	−22.8	31	−19.6	58	−24.0
5	−21.4	32	−18.4	59	−24.8
6	−24.2	33	−19.7	60	−20.4
7	−21.3	34	−23.1	61	−21.3
8	−19.5	35	−18.5	62	−20.9
9	−21.0	36	−26.9	63	−22.0
10	−17.7	37	−16.3	64	−23.9
11	−24.8	38	−24.2	65	−23.6
12	−26.5	39	−20.4	66	−23.8
13	−24.5	40	−33.4	67	−26.6
14	−23.6	41	−22.6	68	−22.3
15	−27.6	42	−20.9	69	−24.9
16	−24.0	43	−25.2	70	−28.3
17	−26.1	44	−25.8	71	−22.0
18	−18.9	45	−14.3	72	−20.8
19	−27.0	46	−23.4	73	−19.5
20	−23.6	47	−28.2	74	−24.0
21	−22.2	48	−22.6	75	−25.3
22	−19.8	49	−21.9	76	−22.3
23	−19.9	50	−19.3	77	−17.2
24	−19.2	51	−12.7	78	−21.8
25	−22.6	52	−21.5	79	−21.1
26	−20.9	53	−24.0	80	−17.2
27	−21.3	54	−22.56	(PRD_002214)	−29.3

The binding mode of (PRD_002214) revealed its engagement with the four pockets of Mpro, resulting in the formation of eight hydrogen bonds (H.B) and four hydrophobic forces. The 2-oxopyrrolidin-3-yl moiety engaged the initial pocket, forming two H.B. The fragment 2-acetamido-3-methylbutanamido)-N-ethyl-4-methylpentanamide occupied the second pocket, establishing four H.B in addition to three hydrophobic forces. Furthermore, the benzyl acetate moiety was strategically located within the third pocket, establishing two H.B. Finally, the 5-methylisoxazole-3-carboxamide moiety was incorporated into the fourth pocket, establishing one hydrophobic force (Figure 5).

Figure 5.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of (PRD_002214) in the M^pro active site.

Compound 12’s binding mode showed an energy score of −26.5 kcal/mol. The receptor’s first pocket was filled by the 2H-chromen-2-one group, which formed a pi-sulfur contact with Met49, one hydrophobic forces with Cys145, and two H.B with His163 and Ser144. The second pocket was filled by the 5-pentyloxy group, which formed two hydrophobic contacts with His41 and Met165. Furthermore, in the third pocket, the 7-pentyloxy group interacted hydrophobically with His41, Cys145, and Leu27 three times (Figure 6).

Figure 6.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 12 in the M^pro active site.

Concerning compound 15, it revealed an energy score of 27.6 kcal/mol. The fourth pocket of M^pro was occupied by butyl-7-yloxyacetate moiety through the formation of three hydrophobic forces with Pro168, Met165, and Leu167. While the third pocket was occupied by butyl-5-yloxyacetate moiety through the formation of three H.B with Cys145 and Gln189 besides two hydrophobic forces with His41 and His164. Likewise, the second pocket was occupied by 4-phenyl moiety through the formation of pi-sigma interaction with Asn142. Finally, the 2H-chromene moiety was buried in the first pocket forming one electrostatic interaction with Glu166 (Figure 7).

Figure 7.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 15 in the M^pro active site.

Respecting the docking results of 17 (energy score of −26.1 kcal/mol), it revealed the formation of eight H-bonding and six hydrophobic forces in the distinct regions within the M^pro pockets. At the outset, methyl-5-yloxypropionate moiety formed two H.B with Glu166 and Gln189 in the first pocket. Moreover, compound 17 was buried in the second pocket through the formation of hydrophobic forces with Leu141 using its 4-phenyl moiety. Likely, 2-oxo-4-phenyl-2H-chromene-7-yloxypropionate moiety formed six H.B with Cys145, Gly143, Arg188, and Asp 187 and five hydrophobic forces with His41, Met165, and Met49 in the third and fourth pockets (Figure 8).

Figure 8.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 17 in the M^pro active site.

Compound 40 occupied the four different pockets of the M^pro with an energy score of −29.0 kcal/mol. The first, second, and third pockets were occupied by 3-yl 2-((4-methyl-2-oxo-3-propyl-2H-chromen-7-yl)oxy)acetate moiety through formation of five H.B with Glu166, Met165, His163, Ser144, and Gln189 besides three hydrophobic forces with His41, Met49, and Cys145. Furthermore, the 10,13-dimethyl-17-(6-methylheptan-2-yl)-2,3,4,7,8,9,10,11,12,13,14,15, 16,17-tetradecahydro-1H-cyclopenta[a]phenanthrene moiety was concealed in the fourth pocket through the formation of four hydrophobic forces with Pro168, Ala191, and Leu50 (Figure 9).

Figure 9.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 40 in the Mpro active site.

The predicted binding orientation of 47 revealed an energy score of −28.2 kcal/mol. The second pocket was occupied with hexahydropyrazino moiety through the construction of two H.B with Gly143 and Asn142. The third pocket was occupied with indole-1,4-dione moiety through the establishment of one H.B with His164 and four hydrophobic forces with His41 and Met49. The fourth pocket was occupied with 3,4-dimethoxyphenethyl moiety through the establishment of five H.B with Thr 24, Thr25, and Thr26 (Figure 10).

Figure 10.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 47 in the M^pro active site.

Compound 67 revealed an energy score of −26.6 kcal/mol. The 7-yl ((benzyloxy)carbonyl)-L-methioninate moiety occupied the first and second pockets establishing two H.B with Glu166 and Asn142. 5-hydroxy-4-oxo-2-phenyl-4H-chromen moiety was buried in the third pocket forming three hydrophobic forces with Met49 and His41 and one pi-sulfur interaction with Met49 (Figure 11).

Figure 11.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 67 in the M^pro active site.

Compound 70 revealed an energy score of −28.3 kcal/mol. The 7-yl ((benzyloxy)carbonyl)-L-alloisoleucinate moiety occupied the first and third pockets forming three H.B with Glu166, Gln189, and His164 besides four hydrophobic forces with Met49, Met165, and His41. On the other hand, the 5-hydroxy-4-oxo-2-phenyl-4H-chromen moiety was buried in the fourth pocket forming three H.B with Pro168, Gln192, and Met165, one pi-sulfur contact with Met165 and one hydrophobic forces with Pro168 (Figure 12).

Figure 12.

(a) Three-dimensional visualization, (b) 2D interaction diagram, and (c) surface mapping of 70 in the M^pro active site.

Binding mode analysis of semi-synthetic inhibitors versus reference molecule (PRD_002214)

The reference molecule, PRD_002214, binds to the SARS-CoV-2 M^pro by occupying all four key binding pockets—S1, S2, S1, and S4—forming eight H.B with key residues such as His163, Glu166, Gln189, and His164, along with four notable hydrophobic interactions involving Met165, His41, and Ala191. The tested semi-synthetic compounds (12, 15, 17, 40, 47, 67, and 70) exhibited highly comparable binding modes. Specifically, compounds 40, 67, and 70 effectively spanned all four binding pockets, similar to PRD_002214. The hydrogen bonding profiles of the tested compounds revealed analogous interactions; for example, compound 12 engaged His163 and Ser144, compound 15 formed interactions with Glu166, Gln189, and Cys145, while compounds 67 and 70 formed multiple H.B with Glu166, Gln189, and His164.

Hydrophobic interactions were also prominently observed in the tested compounds, particularly in compounds 40 and 47, which interacted with residues like Met165 and Pro168. Compounds 67 and 70 consistently showed strong hydrophobic anchoring with Met49, Met165, and His41, mirroring the binding dynamics of the reference ligand. Although the tested compounds lack the covalent α-ketoamide warhead present in PRD_002214, their chromen and benzyloxycarbonyl scaffolds were spatially well-positioned to achieve comparable binding orientations and interactions. Regarding binding free energy, PRD_002214 demonstrated a docking score of −29.3 kcal/mol, while compounds 40 (−29.0 kcal/mol), 47 (−28.2 kcal/mol), and 70 (−28.3 kcal/mol) showed similarly strong binding affinities. These results affirm that the tested semi-synthetic compounds can emulate or even surpass the binding efficiency of the reference molecule through favorable non-covalent interactions.

In silico ADMET analysis

Following the initial docking analysis, seven semisynthetic compounds—12, 15, 17, 40, 47, 67, and 70—displayed the strongest binding affinity toward the SARS-CoV-2 M^pro. To further evaluate their drug-likeness and pharmacokinetic profiles, these top-performing candidates were subjected to ADMET studies using Discovery studio 4.0.⁴⁰ Indinavir, a powerful antiviral drug that inhibits viral protease, functioned as a reference. The outcomes of the ADMET assessment were summarized in Figure 13 and Table 2.

Figure 13.

The anticipated ADMET analysis.

Table 2.

Predicted ADMET for compounds 12, 15, 17, 40, 47, 67, 70, and Indinavir.

Comp.	BBB level^a	Solubility level^b	Absorption level^c	CYP2D6 prediction^d	PPB prediction^e
12	v	v	p	n	m
15	v	l	p	n	m
17	v	l	g	n	m
40	v	v	v	n	m
47	h	v	g	n	m
67	v	l	p	n	m
70	v	l	p	n	m
Indinavir	v	g	m	n	l

BBB level, blood brain barrier level, h = high, v = very low.

Solubility level, v = very low, l = low, g = good.

Absorption level, g = good, m = moderate, p = poor, v = very poor.

CYP2D6, i = inhibitor, n = non inhibitor.

PBB, l means less than 90%, m means more than 90%.

The findings indicated that all semisynthetic compounds have very low levels of BBB penetration except compound 47 which showed high BBB penetration level. Moreover, compound 40 showed a good level of aqueous solubility. Also, the tested semisynthetic compounds exhibited moderate to poor absorption levels except compounds 17 and 47 displayed good absorption levels. Furthermore, all the semisynthetic compounds demonstrated non-inhibitory effect against CYP2D6. These fallouts indicated that these compounds were predicted to be non-hepatotoxic. The plasma protein binding capacity of the tested compounds exceeded 90%. Overall, the mentioned fallouts showed that the examined semisynthetic molecules have an acceptable range of drug-likeness.

In silico toxicity studies

Subsequently, the toxicity profiles of the top seven semi-synthetic compounds 12, 15, 17, 40, 47, 67, and 70 (Table 3) were assessed using Discovery studio software.^41,42 Indinavir, a well-known antiviral protease inhibitor, was utilized as a control drug. The toxicity investigations include eight models; FDA rodent carcinogenicity, carcinogenic potency toxic dose 50% (carcinogenic potency) (TD₅₀),⁴³ rat maximum tolerated dose,^44,45 rat oral LD₅₀,⁴⁶ rat chronic chronic lowest observed adverse effect level (LOAEL),^47,48 ocular irritancy,⁴⁹ and skin irritancy.⁴⁹ These models offered a detailed assessment of the compounds’ potential safety profiles, supporting their suitability for auxiliary preclinical development.

Table 3.

Toxicity properties of compounds 12, 15, 17, 40, 47, 67, 70, and Indinavir.

Comp.	FDA Rodent Carcinogenicity (mouse-female)^a	Carcinogenic Potency TD₅₀ (rat), mg/kg /day	Rat Maximum Tolerated Dose (Feed)^b	Rat Oral LD₅₀^b	Rat Chronic LOAEL^b	Ocular Irritancy	Skin Irritancy
12	n	271.583	0.179	1.865	0.160	None	Mild
15	n	189.187	0.113	2.168	0.155	Mild	Mild
17	n	29.004	0.034	1.310	0.027	Mild	Mild
40	c	2.015	0.014	1.108	0.001	None	Moderate
47	n	0.596	0.038	3.971	0.028	Mild	Mild
67	n	1.247	0.114	0.214	0.012	None	None
70	n	5.550	0.207	0.259	0.012	None	None
Indinavir	n	0.115	0.127	2.239	0.008	Severe	Mild

n = noncarcinogen, c = carcinogen.

Unit: g/kg.

In general, all semisynthetic compounds were predicted to be non-carcinogenic against FDA rodent carcinogenicity model except 40. All semisynthetic compounds showed carcinogenic potency TD₅₀ value ranging from 0.596 to 271.583 mg/kg/day which were higher than Indinavir (TD₅₀ = 0.115 mg/kg/day). With respect to rat maximum tolerated dose, the semisynthetic compounds 12 and 70 showed values of 0.179 and 0.207 g/kg, which is higher than that of Indinavir (rat maximum tolerated dose = 0.127g/kg). All the semisynthetic compounds showed Oral LD₅₀ lower than that of Indinavir (Oral LD₅₀ = 2.239 g/kg) except compound 47 (3.971 g/kg). Besides, all the semisynthetic compounds except 40 had rat chronic LOAEL ranging from 0.012 to 0.160 g/kg body weight which is higher than Indinavir (0.008 g/kg). Finally, only the semisynthetic compounds 67 and 70 showed no irritancy against both models.

DFT studies

To gain deeper insight into the electronic behavior and reactivity of the top candidates, DFT calculations were performed using Discovery studio software. Compounds 12, 15, 17, 67, and 70 were subjected to DFT to explore the relationship between chemical structures, electronic properties and reactivity. The co-crystallized ligand (PRD_002214) was utilized as a standard. DFT parameters comprise total energy,⁵⁰ binding energy, highest occupied molecular orbital (HOMO),⁵¹ lowest unoccupied molecular orbital (LUMO),^51,52 gap energy,⁵³ and dipole moment^54,55

Molecular orbital analysis

The total energies of 12, 15, 17, 67, 70, and PRD_002214 are −1260.506, −1595.898, −1479.088, −2046.196, −1688.257, and −2273.479 eV, respectively. From these results, compound 67 has the highest total energy after the co-crystallized ligand. The other compounds have almost equal total energy. In addition, compounds 12 and 15 exhibited dipole moment values of 4.028 and 5.238, respectively which were almost equal that of the co-crystallized ligand (dipole moment = 4.624). Based on these findings, it could be said that compounds 12, 15, and 67 may display efficient binding against M^pro. The result revealed that compounds 67 and 70 had the lowest gap energy among the tested compounds (0.079 and 0.087 eV, respectively). These gap energies are less than that of the standard compound (0.119 eV). The decreased gap energy of compounds 67 and 70 indicates the higher activity of these compounds (Figure 14 and Table 4).

Figure 14.

Spatial distribution of of molecular orbitals for (a) PRD_002214, (b) 12, (c) 15, (d) 17, (e) 67, and (f) 70.

Table 4.

Thermodynamic parameters of compounds 12, 15, 17, 67, 70, and PRD_002214.

Comp.	Total Energy (eV)	Binding Energy (eV)	HOMO Energy (eV)	LUMO Energy (eV)	Dipole Mag	Band Gap Energy (eV)
12	−1260.506	−11.586	−0.192	−0.079	4.028	0.113
15	−1595.898	−12.379	−0.197	−0.081	5.238	0.116
17	−1479.088	−10.812	−0.203	−0.087	2.815	0.116
67	−2046.196	−12.695	−0.184	−0.105	1.493	0.079
70	−1688.257	−13.222	−0.180	−0.093	1.476	0.087
PRD_002214	−2273.479	−18.489	−0.189	−0.070	4.624	0.119

Molecular electrostatic potential maps

The molecular electrostatic potential (MEP) map provides valuable insight into the electronic distribution of a compound, which in turn influences its potential interactions with the target receptor.⁵⁶ In MEP visualizations, color coding reflects electron density: red regions indicate electron-rich (electronegative) areas, blue regions correspond to electron-deficient (electropositive) areas, and green to yellow regions represent neutral zones. Typically, red regions act as H.B acceptors, blue regions serve as H.B donors, and green/yellow areas are involved in π-π stacking or other hydrophobic interactions. Based on these visual cues, potential interaction sites between ligands and their target receptors can be predicted.⁵⁷

The co-crystallized ligand PRD_002214 displayed eight red zones and four blue zones on its MEP map, suggesting the presence of eight H.B acceptor sites and four donor sites. Among the tested compounds, compound 12 exhibited four red zones, indicating four potential H.B acceptor sites. Compound 15 showed seven red zones, suggesting seven acceptor sites, while compound 17 presented six red zones. Notably, compounds 67 and 70 each showed eight red zones and two blue zones, suggesting the potential to form eight H.B as acceptors and two as donors (Figure 15).

Figure 15.

Molecular electrostatic potential map of (a) PRD_002214, (b) compound 12, (c) compound 15, (d) compound 17, (e) compound 67, and (f) compound 70.

SAR analysis

A detailed SAR evaluation of the top-performing semi-synthetic compounds (12, 15, 17, 40, 47, 67, and 70) revealed consistent structural motifs that drive their inhibitory potency against SARS-CoV-2 Mpro.

Chromen or Coumarin Cores (e.g. compounds 12, 15, 17, 67, and 70) facilitate π–π stacking and hydrogen bonding with catalytic residues like His41 and Cys145. These interactions are clearly depicted in Figure 6 (compound 12), Figure 7 (compound 15), Figure 8 (compound 17), Figure 11 (compound 67), and Figure 12 (compound 70).

Hydrophobic side chains (e.g. pentyloxy, butyloxy, phenyl, or steroidal moieties) effectively occupy S2 and S4 pockets, enhancing binding through van der Waals contacts. These features are visualized in Figure 7 (compound 15) and Figure 9 (compound 40), where bulky aliphatic groups engage Pro168, Met165, and Leu167.

Benzyloxycarbonyl-amino acid derivatives, such as those in compounds 67 and 70, contribute both polar and hydrophobic interactions. Multiple H.B with Glu166, Gln189, and His164 are seen in Figures 11 and 12, reflecting effective pocket occupancy.

Electron-rich regions supporting binding affinity, as evidenced by DFT and MEP analysis, align with the M^pro interaction maps of Figures 11 and 12, where compounds 67 and 70 form stable, multi-modal interactions, consistent with their low HOMO-LUMO gaps and favorable molecular mechanics-generalized born surface area (MM-GBSA) energies.

These SAR trends correlate strongly with structural features required for optimal M^pro inhibition, supporting the rationale for prioritizing these compounds for further in vitro validation.

MD simulation

The stability and dynamic behavior of top candidate compounds 67 and 70 in the Mpro active site were assessed using MD simulations over 150 ns. These simulations illuminated the ligand-receptor complexes’ conformational flexibility and binding stability over time. Substances 67 and 70 maintained different conformations and almost consistent distances from the Mpro’s center of mass throughout the simulation, according to production run analysis. Root mean square deviation (RMSD) for the Mpro_70 complex (red) stabilized at 2.5 Å after 10 ns. In contrast, the Mpro_67 complex (blue) showed two phases: an average RMSD of 2.2 Å for 85 ns, followed by a rise to 3.3 Å for the rest of the simulation (Figure 16(a)). Figure 16(b) demonstrates two states for both compounds. Compound 67 (blue), from 20 ns to 110 ns, shows a stable fluctuation of around 7.5 Å before rising to 10.5 Å. On the other hand, compound 70 (red) shows an average of 4.4 Å during the first 25 ns before rising to 8.2 Å in the rest of the simulation. The significant RMSD values of the ligands result from conformational changes within the binding pocket, as illustrated in Figure 16(c). The figure illustrates the two compounds at 2.7 ns (green sticks), 46.1 ns (cyan sticks), and 124.1 ns (magenta sticks), demonstrating significant motility while preserving their spatial arrangement. The average radius of gyration for the Mpro in the Mpro_70 and Mpro_67 complexes is approximately 22.6 Å, as illustrated in Figure 16(d). Figure 16(e) illustrates that the average solvent-accessible surface area (SASA) values for Mpro in both systems are approximately 15700 Å². Figure 16(f) indicates that the total number of H.B averages approximately 70 in both systems. This suggests that the structure of Mpro is stable. The root mean square fluctuation (RMSF) plot (Figure 16(g)) indicates that both systems exhibit comparable amino acid fluctuations. The majority of amino acids exhibit high stability, with fluctuation values below 2 Å. Exceptions include the N-terminus for both systems (5.7 Å), the Thr45: Asn51 loop (3.1 Å for the Mpro_70 complex and 4.2 Å for the Mpro_67 complex), and the C-terminals (10.5 Å for both systems). Stable binding is evidenced by the consistent separation between the center of mass of each ligand and the Mpro, with an average distance of 21.7 Å observed in both systems (Figure 16(h)).

Figure 16.

(a) RMSD values from the trajectory for the M^pro in M^pro_67 complex (blue) and M^pro_70 complex (red); (b) ligands RMSD values; (c) Shows a comparison between each ligand structure at 2.7 ns (green sticks), at 46.1 ns (cyan sticks), and 124.1 ns (magenta sticks); (d) RoG for the M^pro_67 complex (blue) and M^pro_70 complex (red); (e) SASA for the M^pro_67 complex (blue) and M^pro_70 complex (red); (f) change in the number of hydrogen bonds for the M^pro_67 complex (blue) and M^pro_70 complex (red); (g) RMSF for the M^pro_67 complex (blue) and M^pro_70 complex (red); (h) distance from the center of mass of each ligand and the center of mass of the M^pro.

MM-GBSA studies

Figure 17(a) and (b) shows the MM-GBSA components of the binding free energy for molecule 67 and molecule 70, respectively. The average binding energy of compound 67 is −25.92 kcal/mol, while for molecule 70, it is −30.62 Kcal/Mol indicating a strong interaction for both of them. Electrostatic interactions (−11.73 Kcal/Mol for molecule 67 and −7.34 Kcal/Mol for molecule 70) contribute to binding stability, although considerably less than van der Waals interactions (−34.38 Kcal/Mol for molecule 67 and −40.61 Kcal/Mol for molecule 70). Using a decomposition of the free energy, the contribution of amino acids within 1 nm of each molecule was calculated (Figure 18(a) and (b)). For molecule 67, Met165 (−1.04 Kcal/Mol), Leu167 (−1.2 Kcal/Mol), Pro168 (−1.96 Kcal/Mol), Gln189 (−1.51 Kcal/Mol), The190 (−1.14 Kcal/Mol), Ala191 (−1.48 Kcal/Mol), and Gln192 (−1.2 Kcal/Mol) are the amino acids that have a contribution less than −1 Kcal/Mol. On the other hand, the decomposition of binding free energy for molecule 70 shows that the protein has five amino acids with a contribution of less than −1 Kcal/Mol. These are Met49 (−1.51 Kcal/Mol), Met165 (−1.22 Kcal/Mol), Leu167 (−1.6 Kcal/Mol), Pro168 (−1.97 Kcal/Mol), and Gln189 (−3.23 Kcal/Mol).

Figure 17.

MM-GBSA energetic components and values for the M^pro_67 complex (a) and M^pro_70 complex (b).

Figure 18.

Decomposition of free binding energy of the M^pro_67 complex (a) and M^pro_70 complex (b).

Protein-ligand interaction fingerprint analysis

Protein-ligand interaction fingerprint (ProLIF) functions as a tool in computer-aided drug design and molecular docking investigations, focusing on the examination and characterization of connections between proteins and ligands. This approach comprises creating interaction fingerprints between a ligand and a protein, enabling the assessment of their binding interactions’ strength and characteristics.⁵⁸ These fingerprints allow for the quantification of diverse interaction types, encompassing H.B, hydrophobic forces, and other non-covalent associations. In MD simulations, ProLIF can be effectively employed to scrutinize the dynamic dynamics of protein-ligand complexes over time. This analysis provides valuable insights into the changing nature of interactions between the protein and ligand during the simulation, thereby aiding in comprehending the complex’s stability and binding affinity.⁵⁹ Very long-lasting hydrophobic interactions are detected from Met49 (85.2%), Met165 (98%), Leu167 (87.6%), Pro168 (90.1%), Asp187 (96%), Arg188 (79.8%), Gln189 (99.4%), and Thr190 (91.8%) and 70 (Figures 19 and 20). On the other hand, Met165 (82.8%), Leu167 (84.4%), Pro168 (95%), Gln189 (91.5%), Thr190 (86.8%), Ala191 (83.8%), and Gln192 (83.9%) form hydrophobic interactions with 67.

Figure 19.

ProLIF analysis of the M^pro_67 complex.

Figure 20.

ProLIF analysis of the M^pro_70 complex.

Protein-ligand interactions profile studies

Protein-ligand interactions profiler (PLIP) emerges as a vital bioinformatics tool integral to the examination and visualization of interactions within molecular complexes between a protein and a ligand. This tool furnishes insightful data about the binding interactions and non-covalent connections formed between the protein and the ligand, a crucial aspect for grasping the MD of ligand-receptor associations.³³ Owing to its indispensable role in drug innovation, computational biology, and structure-focused bioinformatics, PLIP finds extensive application in the exploration of protein-ligand complexes and their interactions. By combining PLIP alongside other computational methods molecular docking, MD simulations, and calculations of free energy, a comprehensive comprehension of ligand-protein interactions is achieved, elevating its significance as a pivotal instrument in rational drug design initiatives.⁶⁰ The key frames identified via clustering of the compound 67_ M^pro and compound 70_ M^pro complexes were then run via PLIP to extract the 3D binding conformations as.pse files. The PLIP data extraction yielded three distinct clusters for each compound. The intricate interactions within each cluster have been comprehensively depicted in Figure 21,

Figure 21.

PLIP analysis of the M^pro_67 complex (a) and M^pro_70 complex (b); gray dashed lines: hydrophobic interactions, blue solid lines: H-bonds, orange sticks: molecule 67 or molecule 70, and blue sticks: amino acids of the protein.

Conclusion

This study utilized a comprehensive computer-aided drug design (CADD) approach to identify promising inhibitors of the SARS-CoV-2 M^pro from a curated set of 80 semi-synthetic compounds. Through molecular docking, ADMET profiling, DFT calculations, and 150-nanosecond MD simulations, compounds 67 and 70 demonstrated strong binding affinities, favorable electronic characteristics, and stable interactions within the M^pro active site. While this study is limited to in silico analyses, this study provides a strong foundation for future in vitro and in vivo studies for these two lead structures in COVID-19 drug development.

Experimental

Docking analyses

Docking analyses were done for the pointed 80 semisynthetic candidates against SARS-CoV-2 M^pro (PDB ID: 6LU7) using Discovery studio software.^61–63 (Please refer to the Method section available in the Supplemental Material for more details.)

ADMET analysis

Discovery Studio 4.0 was utilized for the prediction of the ADMET parameters.^64,65 (Please refer to the Method section available in the Supplemental Material for more details.)

Toxicity studies

The software Discovery Studio 4.0 was employed for forecasting TD₅₀, MTD, oral LD₅₀, FDA rat carcinogenicity, LOAEL, as well as irritancy assessments for both ocular and dermal exposures.^66,67 (Please refer to the Method section available in the Supplemental Material for more details.)

Density functional theory

Discovery Studio software was used to compute the DFT parameters, including total energy, binding energy, HOMO, LUMO, gap energy, dipole moment, and electrostatic potential. (Please refer to the Method section available in the Supplemental Material for more details.)

MD simulations studies

A 150-ns classical MD simulation was performed in GROMACS 2021 to examine the stability of the compound 67_ M^pro and compound 70_ M^pro complexes, in addition to the interactions and differences between the holo M^pro and apo M^pro structures.⁶⁸ The input files were prepared by CHARMM-GUI web server’s solution builder module.^69,70 (Please refer to the Method section available in the Supplemental Material for more details.)

Binding free energy calculation using MM-GBSA

The gmx_MMPBSA program was used to evaluate the binding strength of compound 67_ M^pro and compound 70_ M^pro complexes using the MM-GBSA approach.⁷¹ (Please refer to the Method section available in the Supplemental Material for more details.)

PLIP studies

The elbow method was utilized to group the trajectory data obtained from the MD simulation of the compound 67_ M^pro and compound 70_ M^pro complexes.⁷² This approach automatically determined the optimal number of clusters, resulting in four distinct clusters, each represented by a representative frame. To assess the interactions between the compound 67_ M^pro and compound 70_ M^pro complexes in each cluster representative, the PLIP website was utilized. (Please refer to the Method section available in the Supplemental Material for more details.)

Supplemental Material

sj-pdf-1-chl-10.1177_17475198251367070 – Supplemental material for Computer-aided drug discovery of potential semisynthetic inhibitors for SARS-CoV-2 main protease: A multi-phase computational approach

Supplemental material, sj-pdf-1-chl-10.1177_17475198251367070 for Computer-aided drug discovery of potential semisynthetic inhibitors for SARS-CoV-2 main protease: A multi-phase computational approach by Ahmed M Metwaly, Hazem Elkady, Eslam B Elkaeed, Abdelrahman M Saleh, Aisha A Alsfouk, Ibrahim M Ibrahim and Ibrahim H Eissa in Journal of Chemical Research

Footnotes

Acknowledgements

Eslam Elkaeed would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025-031.

ORCID iDs

Ahmed M Metwaly

Hazem Elkady

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R116), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data that support the findings of this study are enclosed in the manuscript and the Supplemental Materials.

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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