The computational analysis in this study was performed using both Windows 10 and Linux Ubuntu 18.10 operating systems. The software utilized includes BIOVIA Discovery Studio 2024 Client 24.1, MGLTools 1.5.7 with AutoDock 4.2.6, PatchDock, PEP-FOLD 3.5, Orca 6.0, Avogadro 1.2.0, Chemcraft, Chimera 1.14, PyMOL 2.5.8, VMD 1.9.2, and GROMACS 2016.3 with the g_mmpbsa package for molecular dynamics and binding free energy calculations. Additionally, web-based platforms such as I-TASSER, SwissADME, pKCSM, ProTox-3.0, and Molinspiration were employed for structure prediction, pharmacokinetic analysis, and toxicity assessment. The computational hardware used in this study consisted of a desktop computer with an Intel Core i5-8500 CPU @ 4.30 GHz (6 cores), 16 GB RAM, a 2 TB HDD, a 120 GB SSD, and an NVIDIA GeForce GTX 1080 Ti GPU. These specifications provided sufficient computational power for molecular docking, density functional theory (DFT) calculations, molecular dynamics simulations, and free energy assessments.

The crystallographic data for essential receptors involved with Plasmodium vivax (identifiers 2BL9 (https://www.rcsb.org/structure/2BL9) (Kongsaeree et al. 2005), 4ZL4 (https://www.rcsb.org/structure/4ZL4) (Hodder et al. 2015)), and Plasmodium falciparum (identifier 1CET (https://www.rcsb.org/structure/1CET) (Read et al. 1999)) were retrieved from the Protein Data Bank (accessible at http://www.rcsb.org/). These structures were resolved at 1.90 Å, 2.37 Å, and 2.05 Å, respectively. Using the Dock Prep tool within BIOVIA Discovery Studio 2024 Client 24.1, the data underwent several modifications, including the elimination of water molecules, superfluous chains, and bound ligands, in addition to the integration of hydrogen atoms and the assignment of partial charges (BIOVIA 2017). Reconstruction of incomplete protein segments was accomplished using tools provided by I-TASSER (https://zhanggroup.org/I-TASSER/) (Zhang 2008).

The pipecolisporin derivatives (analog-1 through analog-6) were sourced in SDF file format from PubChem (available at https://pubchem.ncbi.nlm.nih.gov/) (Fig. 1). The geometry of these ligands was optimized using Avogadro software version 1.2.0, which also facilitated the conversion of these files to .pdb format (Hanwell et al. 2012). Subsequently, these prepared compounds were employed for docking analysis using the BIOVIA Discovery Studio 2024 Client 24.1 software (BIOVIA 2017).

Figure 1. 

A two-dimensional diagram of pipecolisporin derivatives for evaluating structure-activity relationships (SAR).

Density Functional Theory (DFT), grounded in quantum mechanics, offers an exceptionally precise representation of electron distribution within molecules, enabling the computation of multiple properties such as molecular energies, geometries, and electronic characteristics. The Orca 6.0 software package was employed to calculate these quantum mechanical properties (Neese 2022). The electronic properties of the molecules were analyzed in their singlet ground state, under neutral conditions, and without solvents, utilizing the B3LYP (Becke, 3-parameter, Lee-Yang-Parr) functional within the DFT framework, combined with a 6-31G basis set. The DFT outcomes were analyzed using the Chemcraft (graphical program for visualization of quantum) (https://www.chemcraftprog.com/) (Zhurko and Zhurko 2020) and the Avogadro version 1.2.0 programs (Hanwell et al. 2012). This approach allowed for the assessment of the molecule’s reactivity by analyzing key reactivity descriptors, including electron affinity, ionization potential, chemical hardness (η), chemical softness (ζ), electronegativity (χ), electrophilicity (ω), and electronic potential (μ).

The experimental ligands were accommodated within a uniformly sized grid box measuring 64 × 60 × 60 Å for all structures, ensuring coverage of the entire active site. The coordinates for the grid boxes were set based on the natural ligand’s previous positions, providing additional space for ligand flexibility within the docking process: 2BL9 (89.726, 13.626, 34.293), 4ZL4 (−3.040, 99.279, 42.328), and 1CET (36.211, 10.539, 19.830). The Lamarckian genetic algorithm was utilized in conjunction with a rigid receptor framework to identify optimal ligand conformations. Each structure underwent ten separate docking trials using MGLTools 1.5.7 and AutoDockTools (ADT) 4.2.6, from which the most consistent energy and pose across replicates were determined as the definitive outcomes (Forli et al. 2012). Visualization of the docking results was achieved using PyMOL 2.5.8 (Delano 2002) and BIOVIA Discovery Studio 2024 Client 24.1 (BIOVIA 2017), which also facilitated the validation of the docking accuracy by superposing the redocked ligand poses against their co-crystallized forms from the PDB, with RMSD calculations performed in PyMOL 2.5.8 to ensure reliability, aiming for an RMSD under 2 Å (Ariyanto et al. 2023).

Molecular dynamics simulations were performed to evaluate the binding stability, conformational behavior, and interaction modes between the compounds (analog-1 to analog-6) and the receptors (1CET, 2BL9, and 4ZL4). The ligand–receptor complex files were analyzed using GROMACS 2016.3 software for these simulations (Van Der Spoel et al. 2005; Pronk et al. 2013; Abraham et al. 2015). Ligand topologies were generated through the AnteChamber python parser interface (ACPYPE) software (Kagami et al. 2023). For the molecular dynamics (MD) simulation, the complex structures first underwent energy minimization in a vacuum using the steepest descent algorithm for 5000 steps. The solvated complex was then placed in a cubic periodic box with a dimension of 0.5 nm, employing the Simple Point Charge (SPC) water model while maintaining a temperature of 310 K. To further equilibrate the system, Na+ and Cl− ions were introduced to achieve a salt concentration of 0.15 M. Each complex was subjected to a 50 ns simulation under both NVT and NPT ensembles, ensuring constant particle number, temperature, and pressure. Trajectory analysis, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), and solvent-accessible surface area (SASA), was conducted using the GROMACS simulation package via the “WebGRO for Macromolecular Simulations” online server (https://simlab.uams.edu/) (Paz et al. 2004). Graphs were created using the Xmgrace tool from Microsoft Excel 2013 and Grace Software 5.9 (Turner 2005).

The outcomes of molecular dynamics simulations were employed to determine the binding free energies between the protein-ligand complexes using the MM-PBSA method (Kumari et al. 2014; Wang et al. 2018; Ren et al. 2020). This technique is a reliable and efficient approach for estimating free energy and understanding molecular recognition, particularly in protein-ligand interactions. Ligand binding energies were computed using the g_mmpbsa and MMPBSA.py script from the GROMACS suite package. The core concept behind this method is to decompose the total free energy of the complex into its various components, including solvation, van der Waals, and electrostatic contributions. The binding free energy is calculated using the equation:

This equation is further expanded as ∆Gbind = ∆GMM + ∆GPB + ∆GSA − T∆S. In this formula, ∆GMM represents the molecular mechanics interactions, which are the sum of electrostatic and van der Waals forces. ∆GPB and ∆GSA denote polar and non-polar solvation energies, respectively, while T∆S accounts for the entropic contribution to the binding free energy.

The ProTox-3.0 (Prediction of TOXicity of chemicals) (https://tox.charite.de/protox3/) server was employed to forecast the toxicity of the selected compounds, with the input provided in the form of canonical SMILES notation (Banerjee et al. 2018). For calculating the absorption, distribution, metabolism, and excretion (ADME) properties, the pKCSM (https://biosig.lab.uq.edu.au/pkcsm/) and SwissADME (http://www.swissadme.ch/) platforms, both freely accessible online, were utilized (Daina et al. 2017; Pires et al. 2015). These tools enabled the prediction of various pharmacokinetic and pharmacodynamic attributes of the compounds. The biological activity of three compounds was assessed using the Molinspiration (https://www.molinspiration.com/) server, where a bioactivity score of > 0 indicated biologically active compounds, a range of −5.0 to < 0 signified moderate activity, and a score of <-5.0 indicated biologically inactive compounds.

Frontier molecular orbital (FMO) analysis is a quantum chemistry computational method used to examine the energy levels, configuration, and electron distribution of a molecule’s highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). HOMO-LUMO analysis is performed to determine the reactivity of molecular orbitals in organic compounds (Tanaka and Chujo 2021). Molecules with minimal or non-existent HOMO-LUMO gaps are typically more reactive. This area of chemistry is explored for potential inhibitors. FMO calculations assess pipecolisporin derivatives regarding their ionization potential and electron affinity. The electronic descriptors examined include parameters like EHOMO, ELUMO, ∆Egap, ionization potential, electron affinity, electronegativity, chemical potential, hardness, softness, electronic potential, and electrophilicity, with the results presented in Fig. 2, Table 1. In quantum chemistry, electronic energy (Eh) plays a critical role in computational techniques for analyzing molecular behavior. Differences in Eh reveal variations in the stability and bonding interactions of these compounds. Higher energy values indicate more reactive molecules. Analog-3 registered the lowest energy at −2431.659809 Eh, while analog-1 showed the highest energy at −2264.156 Eh. Additionally, analog-3 exhibited the highest dipole moment at 9.9702 D, while analog-1 recorded the lowest at 4.2718 D. Meanwhile, the other pipecolisporin analogs displayed intermediate dipole moments: 9.7271 D (analog-2), 7.6997 D (analog-4), 5.0153 D (analog-5), and 6.4377 D (analog-6). These dipole moment differences indicate variations in charge distribution and polarity, with analog-3 being highly polar and analog-1 relatively less polar.

Figure 2. 

The diagram illustrates the arrangement of molecular orbitals in their lowest energy configuration.

The EHOMO values spanned from −0.18243 to −0.20167 eV, with analog-3 showing the lowest and analog-5 reaching the highest. ELUMO values ranged between −0.00058 and −0.01193 eV, where analog-6 exhibited the lowest and analog-5 the highest readings. The energy gap (∆Egap) extended from −0.18185 to −0.19202 eV, with analog-1 demonstrating the broadest gap. Ionization potential fluctuated from 0.18243 eV to 0.20167 eV. Electronegativity varied from 0.896109 to 0.924093, with analog-4 registering the highest. Chemical potential values were found between 0.091505 and 0.10619 eV. Hardness ranged from 0.090925 to 0.09601 eV, with analog-1 having the greatest hardness. Softness spanned from 5.207790855 to 5.499037668 eV−1. Electronic potential oscillated between −0.091505 and −0.10557 eV. Electrophilicity ranged from 0.04604435 to 0.059509958 eV.

The key compounds display distinct electronic characteristics when compared with each other. Notably, analog-3 features a significantly lower EHOMO and a notably tighter energy gap, suggesting it behaves differently in terms of electronic structure relative to the others. Analog-6, with the lowest ELUMO, also demonstrates higher values for electronegativity and softness, highlighting unique patterns in electron arrangement and reactivity. Analog-5, on the other hand, has the most elevated EHOMO and ELUMO values, alongside a higher ionization potential, pointing to a unique ionization profile among the group. Furthermore, their variances in electronegativity and electrophilicity signal distinct chemical reactivity and interaction potentials, particularly in contrast with the reference molecule, analog-1.

Molecular docking analysis was conducted to evaluate the interaction potential between pipecolisporin derivative compounds and specific target proteins. The docking process was carried out using MGLTools 1.5.7 and AutoDockTools (ADT) 4.2.6 to determine the binding energy values, which serve as indicators of molecular affinity. The selected target proteins, 2BL9, 4ZL4, and 1CET, are crucial in malaria treatment and possess well-defined binding sites within their crystalline structures, as documented in the .pdb file. The docking simulation was performed using a grid dimension of 64 × 60 × 60 Å. The results demonstrated that all compounds exhibited binding energies below −6.06 kcal/mol (Table 2), with the most stable complexes being Analog-3–2BL9 (−13.02 kcal/mol), Analog-3–4ZL4 (−8.07 kcal/mol), and Analog-3–1CET (−13.02 kcal/mol). Notably, the majority of ligands analyzed displayed stronger binding affinities compared to the threshold value. This suggests that pipecolisporin derivatives possess significant inhibitory potential against the selected target proteins, making them promising candidates for further investigation in the development of antimalarial agents.

Based on previous studies conducted on the original, unmodified pipecolisporin, this compound exhibited its highest binding affinity towards the 2BL9 target, with a value of −10.26 kcal/mol and an inhibition constant of 29.90 nM, indicating strong inhibitory potential. However, for the 1CET and 4ZL4 targets, pipecolisporin showed lower binding affinities of −6.59 kcal/mol and −5.38 kcal/mol, respectively (Kurniaty et al. 2025). These results suggest that pipecolisporin’s interaction with 1CET and 4ZL4 is relatively weaker compared to its modified analogs. Analog-3, a structurally modified derivative of pipecolisporin, demonstrated a significant improvement in binding affinity across all targets. Specifically, for 2BL9 and 1CET, analog-3 formed more stable complexes, with binding affinities exceeding −13.02 kcal/mol. The more negative binding affinity values indicate a stronger and more stable ligand-target interaction, which likely contributes to better inhibitory effects. This striking difference highlights analog-3 as a more potent candidate compared to the original pipecolisporin and suggests its potential as a lead compound for further drug development.

When compared with previously studied small molecules, analog-3 demonstrated superior binding affinity, particularly against 2BL9. Studies have shown that some small molecules exhibited binding affinities of −8.70 kcal/mol to −8.50 kcal/mol for 2BL9, which are significantly weaker than Analog-3’s binding affinity of −13.02 kcal/mol. This reinforces analog-3 as a highly potent dihydrofolate reductase inhibitor. However, against 4ZL4, certain small molecules outperformed analog-3, with reported binding affinities ranging from −9.60 kcal/mol to −8.30 kcal/mol, compared to analog-3’s −8.07 kcal/mol. Similarly, for 1CET, analog-3 (−13.02 kcal/mol) exhibited a stronger binding affinity than chloroquine (−6.30 kcal/mol) but was weaker than some other reported small molecules, which had affinities of −9.10 kcal/mol and −7.90 kcal/mol (Lobato-Tapia et al. 2023; Moreno-Hernández et al. 2023). These findings suggest that while analog-3 is a strong inhibitor of 2BL9, its interaction with 4ZL4 and 1CET may require structural modifications to improve its efficacy. Enhancing its molecular interactions through targeted modifications could increase its potential as a multi-target antimalarial agent. Further studies should explore how structural refinements could optimize analog-3 for broader therapeutic applications.

Fig. 3 displays the binding sites and orientation poses of the three complexes. The interactions between these compounds and the target proteins were mapped and visualized using PyMOL 2.5.8 and BIOVIA Discovery Studio 2024 Client 24.1. The binding affinity values indicate the strength of the interaction between the ligand and the target protein, with more negative values reflecting stronger interactions. Among all the analogs, analog-3 exhibited the highest binding affinity towards the 2BL9 target with a value of −13.02 kcal/mol, as well as a very low inhibition constant of 284.71 pM, indicating a high potential for inhibition. By comparison, analog-1 and analog-2 showed lower binding affinity values, ranging from −10.67 to −11.05 kcal/mol, but still demonstrated fairly strong interactions with the target proteins. For the 1CET and 4ZL4 targets, Analog-3 also had better binding affinity values compared to the other analogs, with values of −7.28 kcal/mol and −8.07 kcal/mol, respectively. The low inhibition constants for analog-3 suggest that this compound has potential as an effective inhibitor in protein-ligand binding processes. In contrast, the higher inhibition constants in the other analogs indicate that they may be less effective as inhibitors compared to analog-3.

Figure 3. 

The two-dimensional and three-dimensional depictions illustrate the hydrogen bonds and hydrophobic interactions between the ligands and the binding cavities of the receptors.

In each visualization, the position of the ligand within the receptor’s binding pocket is clearly depicted, highlighting how analog-3 interacts with the surrounding amino acids. The visible hydrogen bonds help stabilize the ligand-receptor interaction, while hydrophobic interactions support the ligand’s affinity for the protein. In the complex with 2BL9, analog-3 exhibits several strong hydrophobic interactions, particularly with residues LEU45 and MET54. In the interaction with 4ZL4, hydrogen bonding is more dominant, providing a balance between stability and flexibility of the complex. The visualization for 1CET shows a combination of hydrogen and hydrophobic bonds, offering a comprehensive view of how analog-3 modulates binding in various protein environments. This visualization is crucial for understanding molecular binding mechanisms and guiding further drug development.

Following the assessment of protein-ligand interactions and the achievement of favorable results, molecular dynamics (MD) analysis was performed on the ligands exhibiting the highest binding affinities to evaluate the stability of the protein-ligand complexes (Rizkita et al. 2024). MD simulations were carried out for a duration of 500 ns on the complexes with the lowest binding affinity, specifically analog-2–2BL9, analog-3–2BL9, analog-2–4ZL4, analog-3–4ZL4, analog-2–1CET, and analog-3–1CET. To evaluate the structural stability and fluctuations of these complexes, trajectory analyses were conducted using root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), and solvent-accessible surface area (SASA) metrics. These analyses provide insights into the dynamic behavior of the complexes over time, revealing any significant conformational changes. Understanding these fluctuations is essential for predicting the binding stability and potential efficacy of the ligands in practical applications.

The RMSD of the protein backbone was tracked throughout the 500 ns simulation to assess the structural stability and fluctuations (Fig. 4). In the case of the Analog-2–2BL9 complex, notable deviations were observed, with RMSD peaking at around 0.3 nm during the initial 100 ns, after which the system gradually stabilized and maintained steady values for the remainder of the simulation. Conversely, the analog-3–2BL9 complex reached equilibrium earlier with fewer fluctuations, indicating a higher degree of structural stability. For the analog-2–4ZL4 complex, RMSD fluctuated between 0.2 and 0.4 nm for the first 200 ns before settling into a stable conformation. Meanwhile, the analog-3–4ZL4 complex showed consistent stability from the start of the simulation. The analog-2–1CET complex exhibited variations up to 0.25 nm before stabilizing around the 100 ns mark, whereas the Analog-3–1CET complex displayed smoother transitions and remained more stable throughout the entire period. Overall, analog-3 complexes consistently demonstrated superior stability compared to analog-2, implying stronger binding interactions and better affinity for the target proteins.

Figure 4. 

The RMSD and RMSF graphs for the 500 ns molecular dynamics simulation of Analog-2–2BL9, Analog-3–2BL9, Analog-2–4ZL4, Analog-3–4ZL4, Analog-2–1CET, and Analog-3–1CET.

The RMSF analysis provides insights into residue flexibility across the 500 ns simulation, highlighting key differences between analog-2 and analog-3 complexes. In the analog-2–2BL9 complex, fluctuations peaked around residue 150 at 0.5 nm, whereas analog-3–2BL9 exhibited lower fluctuations, suggesting better stability. Similarly, analog-2–4ZL4 showed high RMSF values near residues 50, 200, and 400, while analog-3–4ZL4 had a smoother fluctuation pattern. For analog-2–1CET, a notable peak around residue 250 reached nearly 0.45 nm, whereas analog-3–1CET displayed more uniform fluctuations. In all cases, analog-3 derivatives exhibited reduced fluctuations, indicating stronger binding stability. The consistently lower RMSF values in analog-3 complexes support the RMSD findings, reinforcing their structural rigidity. High RMSF peaks in analog-2 suggest flexible regions or surface-exposed loops that may impact binding efficiency. These variations in residue flexibility further emphasize differences in the dynamic behavior of the two analog series.

The radius of gyration (Rg) values were analyzed to assess the compactness and structural flexibility of the protein-ligand complexes over the simulation time (Fig. 5). For the analog-2–2BL9 complex, the Rg values initially decreased during the first 10 ns before stabilizing for the remainder of the 500 ns simulation. A similar trend was observed for the analog-3–2BL9 complex, but with less fluctuation, indicating improved structural stability. In the case of analog-2–4ZL4, the Rg values also dropped after 10 ns, followed by minor fluctuations, while analog-3–4ZL4 maintained steady Rg values throughout the simulation, indicating more consistent structural integrity. The analog-2–1CET complex showed an increase in Rg after 15 ns, which then remained stable, whereas the analog-3–1CET complex maintained lower and more stable Rg values over the entire simulation.

Figure 5. 

The radius of gyration (Rg) and solvent-accessible surface area (SASA) graphs for the 500 ns molecular dynamics simulations of Analog-2–2BL9, Analog-3–2BL9, Analog-2–4ZL4, Analog-3–4ZL4, Analog-2–1CET, and Analog-3–1CET.

Solvent-accessible surface area (SASA) values were also plotted over time to evaluate the exposure of the complex to the solvent (Fig. 5). In all complexes, SASA values decreased during the initial 20 ns of the simulation. The analog-3–1CET and analog-3–4ZL4 complexes exhibited a greater reduction in SASA compared to their analog-2 counterparts, indicating that the analog-3 complexes were more compact and less exposed to the solvent. Conversely, the analog-2–2BL9 complex retained higher SASA values than the analog-3–2BL9 complex, suggesting a looser structure. These results, based on both Rg and SASA data, suggest that the analog-3 complexes, especially with 4ZL4 and 1CET, displayed superior structural stability and compactness compared to their analog-2 counterparts.

The MM-PBSA approach was utilized to evaluate the interactions and binding energies between pipecolisporin compounds and their receptor targets. These simulations provided an estimation of how analog-2 and analog-3 bind with three receptors: 2BL9, 4ZL4, and 1CET (Table 3). In this analysis, the binding energies serve as an indicator of the strength of the compound-receptor interactions. Stronger negative values generally reflect more stable and favorable interactions, though they do not directly indicate binding presence or absence. It is important to recognize that these energy values are qualitative, as factors like entropy are not fully accounted for (Mishra et al. 2022). The binding energies for analog-2 and analog-3 varied across receptor targets. For instance, analog-2 showed a binding energy of −135.171 kJ/mol with 2BL9, suggesting a robust interaction, while analog-3 displayed a slightly higher binding energy of −130.053 kJ/mol. This trend is observed in other receptor targets, where analog-3 consistently exhibits higher binding energies than analog-2, yet both show strong interactions.

Additional energy factors, such as van der Waals, electrostatic, polar solvation, and SASA energy, highlight the different interaction mechanisms. For example, the electrostatic interaction for analog-3 with 2BL9, at −62.626 kJ/mol, is notably stronger than that of analog-2 at −35.206 kJ/mol. The MM-PBSA analysis adds depth to molecular docking by offering a clearer and more precise calculation of binding energies, incorporating solvation effects and flexibility of the compounds. The binding energy calculations show that both analog-2 and analog-3 form highly stable complexes with the receptors, positioning them as promising leads for further drug development, especially considering their strong interactions across different receptor targets.

In terms of pharmacological properties, all the analogs violate Lipinski’s rule of five, with common violations related to high molecular weight (MW>500) and an excessive number of hydrogen bond donors and acceptors (Table 4). For example, analog-2 and analog-3 have 6 and 7 hydrogen bond acceptors, respectively, exceeding the typical threshold in Lipinski’s rule. This indicates that while these analogs may have strong biological interaction potential, their oral bioavailability could be problematic. Additionally, the high topological polar surface area (TPSA) across all analogs (e.g., 234.71 Ų for analog-4) further complicates their absorption through cell membranes, posing a barrier to efficient drug delivery. Despite these challenges in drug-likeness properties, it is important to note that none of the analogs trigger any pan assay interference compounds (PAINS) alerts, meaning they are unlikely to produce false positives in biological assays. Therefore, while there are concerns related to pharmacokinetics and bioavailability, pipecolisporin analogs remain promising candidates for further research, with necessary modifications and optimizations to improve their pharmacological profiles and bioavailability.

The toxicity profiles of the pipecolisporin analogs (analog-1 through analog-6) reveal significant insights regarding their safety and potential pharmacological effects (Fig. 6). Each analog is associated with a predicted LD₅₀ value, indicating the amount required to cause lethality in 50% of a test population. For instance, analog-1 and analog-6 have a predicted LD₅₀ of 1200 mg/kg and fall under toxicity class 4, suggesting moderate toxicity. In contrast, analog-2, analog-3, and analog-5 are classified as toxicity class 3 with a lower predicted LD₅₀ of 300 mg/kg, indicating a higher risk of toxicity. Analog-4 also exhibits a predicted LD₅₀ of 1250 mg/kg and belongs to toxicity class 4, further emphasizing the variability in toxicity across the analogs. In addition to LD₅₀ values, the average similarity across all analogs is around 64–65%, with a consistent prediction accuracy of approximately 68.07%. This similarity index is crucial as it highlights how closely related these analogs are to known compounds with established toxicological profiles. The predictions suggest that while some analogs may present lower toxicity risks, others warrant careful evaluation due to their higher toxicity classifications. Overall, these findings provide essential guidance for further studies on the safety and efficacy of these compounds in drug development.

Figure 6. 

Predicted toxicity profiles and ADME characteristics.

From the pharmacokinetic and toxicity evaluation, all analogs showed low gastrointestinal absorption, an inability to cross the blood-brain barrier, and functioned as P-gp substrates, which could affect bioavailability. Although they are not hepatotoxic or cardiotoxic, all analogs have neurotoxic potential and toxicity effects on the respiratory and immune systems. Their biological activity against targets such as GPCRs, ion channel modulators, and kinases showed low potential, although some analogs exhibited slight activity as protease inhibitors. This aligns with predictions made using Molinspiration’s Bioavailability Suite, which utilizes chemical descriptors and machine learning/QSAR models to predict bioactivity scores. Based on the data in Table 5, all analogs demonstrated low bioactivity as GPCR ligands, ion channel modulators, and kinase inhibitors, with values indicating weak or insignificant activity. However, they did exhibit slight activity as protease inhibitors, which could be explored further for potential therapeutic applications. Despite these low activity scores, the analogs show some promise, particularly as enzyme inhibitors. However, their overall therapeutic potential remains limited without further structural optimization. These findings suggest that while immediate applications are limited, modifications could enhance their viability in specific therapeutic areas.

The chart in Fig. 7 shows a breakdown of the biological activity of six different analogs, focusing on Family A G protein-coupled receptors (GPCR), protease, and other entities. All analogs exhibit significant activity toward GPCRs, with analog-6 showing the highest at 93.3% and analog-1 the lowest at 66.7%. This suggests that GPCRs are the primary target for these analogs, which could influence cell signaling pathways. Protease activity is present in all analogs, but in smaller proportions, ranging from 13.3% in analog-1 to 6.7% in analog-6. Protease inhibition can be important for therapeutic interventions, particularly in diseases where protease activity is detrimental. Some analogs, specifically Analog-1 through Analog-4, display activity toward an entity labeled “Eraser,” potentially linked to gene regulation or epigenetic mechanisms. This activity varies, indicating a secondary but relevant biological role for these analogs. Interestingly, only analog-4 exhibits activity toward an unclassified protein, suggesting unique characteristics that warrant further exploration. Overall, while GPCR activity dominates, variations in protease and “Eraser” activity may provide additional therapeutic opportunities. Each analog shows distinct activity profiles that could be leveraged for specific medical applications depending on the targets involved.

Figure 7. 

Comparison of biological activity profiles of six analogs targeting GPCRs, proteases, and other entities.