Design and In Silico Evaluation of Chalcone–Schiff Base Hybrids as Multi-Target Agents for Inflammatory, Cancer, and Neurodegenerative Diseases

Chronic disorders such as cancer, inflammatory diseases, and neurodegenerative conditions involve complex pathological pathways regulated by multiple molecular targets. Therapeutic strategies directed toward a single target often show limited long-term efficacy and may lead to drug resistance or reduced clinical response. Therefore, the development of multi-target-directed ligands has emerged as an effective approach for the management of complex diseases.

Chalcone derivatives are well known for their diverse pharmacological activities, including anti-inflammatory, anticancer, antioxidant, and neuroprotective effects. Similarly, Schiff base compounds exhibit significant biological properties such as enzyme inhibition and cytotoxic activity. The structural hybridization of chalcone and Schiff base scaffolds may enhance binding interactions and improve biological performance across multiple therapeutic targets.

The present study evaluates the multi-target therapeutic potential of newly designed chalcone–Schiff base hybrids using molecular docking against major disease-related proteins involved in inflammation, cancer progression, apoptosis regulation, neurodegeneration, and cellular signaling.

2. MATERIALS AND METHODS

2.1 Ligand Preparation

Ten chalcone–Schiff base hybrids (CSB1–CSB10) were designed and structurally optimized prior to docking analysis.

The designed molecules were generated by modifying the para-amino group of the aminochalcone scaffold through Schiff base formation with structurally diverse aromatic and heteroaromatic aldehydes using ChemSketch software. The imine (–CH=N–) linkage was constructed by connecting the aldehyde carbon to the amino nitrogen, followed by adjustment of bond order and removal of the carbonyl oxygen to obtain the desired chalcone–Schiff base hybrids.

Ten derivatives (CSB1–CSB10) were designed using different aldehyde substituents to introduce electronic and structural diversity, including hydroxylated, nitro-substituted, electron-donating, heterocyclic, polyaromatic, and natural product-based moieties. This systematic substitution strategy enabled evaluation of structure–activity relationships across multiple biological targets.

All structures were cleaned using the Clean Structure function and converted into three-dimensional conformations using the 3D optimization tool available in ChemSketch. The optimized molecules were saved in MOL format and subsequently converted to PDB format for docking studies. Each ligand was examined for correct valency, appropriate protonation state, and preservation of the (E)-chalcone configuration. The prepared ligand structures were then used for molecular docking analysis .

Table 1. Chemical structure characteristics and classification of chalcone–Schiff base hybrid compounds (CSB1–CSB10) showing imine substitution pattern, molecular formula, and  molecular weight.

Figure 1. Three-dimensional optimized structures of chalcone–Schiff base hybrid compounds (CSB1–CSB10). The structures illustrate the common chalcone backbone with systematic variation in imine substituents. All compounds were geometry-optimized prior to docking analysis.

2.2 Target Proteins

The following proteins were selected based on their clinical relevance:

• COX-2 (PDB ID: 1CX2) – inflammation
• TNF-α (PDB ID: 2AZ5) – inflammatory cytokine
• AChE (PDB ID: 4EY7) – Alzheimer’s disease
• BCL-2 (PDB ID: 1W51) – apoptosis regulation
• Topoisomerase II (PDB ID: 1ZXN) – cancer target
• BACE1 (PDB ID: 2W3L) – amyloid formation
• EGFR (PDB ID: 1M17) – cancer signaling

Protein Preparation and Energy Minimization

The three-dimensional crystal structures of target proteins were retrieved from the RCSB Protein Data Bank. Protein preparation was carried out using BIOVIA Discovery Studio Visualizer. All co-crystallized ligands, water molecules, and heteroatoms not involved in binding were removed. Missing hydrogen atoms were added, and appropriate bond orders were assigned.

Energy minimization of the prepared proteins was performed using the Clean Geometry and structure optimization tools available in Discovery Studio to remove steric clashes and obtain a stable conformation prior to docking.

2.3 Molecular Docking

Molecular docking was performed using the SwissDock web server (2024 version), which is based on the AutoDock Vina scoring function and the EADock DSS algorithm. Docking simulations were carried out in accurate (slow) mode using a blind docking approach to explore the entire protein surface. Default parameters were applied for grid generation, clustering, and scoring. The best binding conformations were selected based on the lowest estimated binding free energy (ΔG, kcal/mol) and favorable interactions with key active-site residues[31].

Docking Validation

The docking protocol was validated by comparing the binding orientation of standard inhibitors with their reported binding interactions in the literature. The observed interaction patterns with key active-site residues confirmed the reliability of the docking methodology.

2.4 ADME Analysis

Drug-likeness and pharmacokinetic properties were predicted using SwissADME.

3. RESULTS

3.1 Docking Analysis

The docking results revealed significant multi-target activity of the synthesized compounds. Several molecules showed binding affinities comparable to or better than standard inhibitors.

The binding affinities of the designed compounds against the selected targets are summarized in Table 2.

Figure 2. Three-dimensional docking poses of selected lead compounds within the active sites of target proteins. (A) CSB6 in the BCL-2 binding groove showing hydrogen bonding and hydrophobic interactions. (B) CSB4 in the COX-2 active site demonstrating deep channel penetration and interaction with key catalytic residues. (C) CSB9 in the Topoisomerase II catalytic pocket showing stable multi-point binding interactions.

Table 2. Molecular docking binding energies (ΔG, kcal/mol) of CSB compounds against selected therapeutic targets

Figure 3. Binding affinity heatmap of chalcone–Schiff base hybrids (CSB1–CSB10) across seven therapeutic targets (COX-2, TNF-α, AChE, EGFR, BCL-2, Topoisomerase II, and BACE1). Color intensity represents binding strength (kcal/mol), with darker shades indicating stronger binding. Lead compounds (CSB4, CSB6, and CSB9) showed superior multi-target performance.

3.2 Lead Compound Identification

Based on the docking scores across multiple targets, CSB4, CSB6, CSB7, and CSB9 were identified as lead candidates. CSB4 showed the highest affinity toward COX-2, CSB6 exhibited strong binding to TNF-α, BCL-2, and EGFR, CSB9 demonstrated superior interaction with Topoisomerase II and AChE, and CSB7 showed the best affinity for BACE1. These results indicate that the selected compounds possess favorable multi-target binding profiles.

3.3 Binding Site Interaction Analysis and Standard Comparison

Detailed interaction analysis of the lead compounds (CSB4, CSB6, CSB9, and CSB7) was performed to evaluate their binding orientation within the active sites and to compare their performance with standard inhibitors.

COX-2 (PDB ID: 1CX2)

CSB4 demonstrated the strongest binding among the series and showed deep penetration into the hydrophobic substrate channel. The ligand formed hydrogen bonding interactions with ARG120 and SER530, along with π–π stacking and hydrophobic contacts involving TYR355, VAL349, and LEU352. The binding energy of CSB4 (−9.841 kcal/mol) was superior to the standard drug Celecoxib (−9.398 kcal/mol), indicating strong anti-inflammatory potential.

TNF-α (PDB ID: 2AZ5)

CSB6 exhibited the highest affinity toward TNF-α and formed multiple hydrogen bonds with the acidic hotspot residues ASP82, ASP86, and GLU90. Additional stabilization was provided through aromatic interactions with TYR87 and TRP129. The docking score of CSB6 (−9.408 kcal/mol) exceeded that of the reference inhibitor SPD304 (−8.850 kcal/mol).

BCL-2 (PDB ID: 1W51)

CSB6 showed deep insertion into the BH3-binding groove, forming hydrogen bonds with ASN143 and ARG146, along with strong π–π interactions with PHE101, TYR105, and PHE109. Extensive hydrophobic contacts with LEU134, VAL145, and ALA149 further stabilized the complex, supporting its potential as an apoptosis-inducing anticancer candidate.

Topoisomerase II (PDB ID: 1ZXN)

CSB9 demonstrated the highest binding affinity and interacted with key catalytic residues ASP480 and HIS729, along with metal coordination support near ASP584. The compound also established aromatic stacking interactions with DNA bases and hydrogen bonding with phosphate groups, indicating strong enzyme inhibition potential.

AChE (PDB ID: 4EY7)

CSB9 showed deep penetration within the catalytic gorge, interacting with the catalytic triad residues SER203 and HIS447. Additional stabilization was observed through interactions with peripheral site residues ASP72 and GLU81, suggesting effective dual-site binding and potential neuroprotective activity.

BACE1 (PDB ID: 2W3L)

CSB7 exhibited the strongest interaction with the catalytic aspartate dyad ASP32 and ASP228. The ligand was further stabilized by hydrophobic interactions within the S1 pocket (TRP76, PHE108, ILE110) and polar contacts with TYR71 and THR72, indicating promising anti-Alzheimer’s potential.

EGFR (PDB ID: 1M17)

CSB6 showed strong binding within the ATP-binding pocket, forming interactions with the catalytic residue LYS745 and hinge region residue ASN842. Hydrophobic contacts with the gatekeeper region (LEU764, VAL769, MET769) contributed to complex stability. Although slightly lower than the standard Erlotinib (−9.356 kcal/mol), the binding affinity of CSB6 (−8.788 kcal/mol) indicated significant inhibitory potential.

Overall, The interaction analysis indicates that the lead compounds form stable hydrogen bonding and hydrophobic interactions with key active-site residues, supporting their observed docking performance.

Figure 4. Two-dimensional ligand–protein interaction maps of representative lead compounds. (A) CSB6–BCL2 complex showing multiple hydrogen bonds and π–π interactions within the BH3-binding groove. (B) CSB4–TNF-α complex highlighting critical molecular interactions at the subunit interface; the map illustrates specific binding motifs that stabilize the CSB4-ligand within the TNF-alpha binding pocket, potentially modulating its cytokine activity. (C) CSB9–Topo II complex illustrating hydrogen bonding and aromatic interactions within the Topoisomerase II catalytic site.

3.4 ADME and Drug-Likeness Analysis

Drug-likeness and pharmacokinetic properties were predicted using the SwissADME web server. The evaluated parameters included molecular weight, lipophilicity (LogP), topological polar surface area (TPSA), hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), rotatable bonds, gastrointestinal (GI) absorption, blood–brain barrier (BBB) permeability, CYP3A4 inhibition potential, plasma protein binding, and Lipinski’s rule of five compliance.

The predicted ADME parameters for all compounds are presented in Table 3.

Table 3. Predicted ADME and drug-likeness properties of chalcone–Schiff base hybrids (CSB1–CSB10) calculated using SwissADME, including Lipinski’s rule of five parameters, pharmacokinetic behavior, and bioavailability indicators.

The predicted ADME profiles indicated favorable pharmacokinetic characteristics for all chalcone–Schiff base hybrids (CSB1–CSB10). All compounds satisfied Lipinski’s rule of five with zero violations and molecular weights below 500 Da, suggesting good oral drug-likeness. The lipophilicity values (LogP: 2.86–4.30) were within the optimal range, indicating balanced hydrophilic–lipophilic properties that support membrane permeability. TPSA values (55.8–95.48 Ų) and acceptable numbers of hydrogen bond donors and acceptors further suggested good absorption potential. All compounds showed high predicted gastrointestinal absorption. Several compounds (CSB1–CSB4, CSB7, and CSB8) were predicted to cross the blood–brain barrier, indicating possible central nervous system activity, which is relevant for targets such as AChE and BACE1. Most derivatives exhibited high plasma protein binding, which may contribute to prolonged systemic exposure. Although some compounds showed potential CYP3A4 inhibition, the overall pharmacokinetic profiles suggest that the CSB series possesses favorable drug-likeness and bioavailability characteristics, supporting their development as promising multi-target therapeutic candidates.

Figure 5. SwissADME bioavailability radar plots of chalcone–Schiff base hybrid compounds (CSB1–CSB10) showing key physicochemical parameters including lipophilicity, size, polarity, solubility, flexibility, and saturation. Most compounds fall within the optimal drug-likeness region, indicating favorable pharmacokinetic profiles.