Free Energy Calculations Research Articles

Unraveling modulation effects on albumin synthesis and inflammation by Striatin, a bioactive protein fraction isolated from Channa striata: In silico proteomics and in vitro approaches

Hypoalbuminemia, associated with inflammation in severely ill patients, can emerge due to decreased albumin production. Transforming growth factor-beta (TGF-β) and nuclear factor-kappa B (NF-κB) are critical signaling pathways responsible for decreased albumin expression. This study explores the protein content and modulation effects of Striatin on albumin synthesis and inflammation, employing in silico proteomics and in vitro investigations. In the in silico proteomics realm, LC/MS-MS protein sequencing, 3D modeling, protein-protein docking simulations, 100 ns molecular dynamics (MD) simulations, and MM/PBSA binding free energy calculations were carried out. Complementing this, in vitro studies examined Albumin gene expression and extracellular secretion in HepG2 cells subjected to lipopolysaccharides-induced hypoalbuminemia. Furthermore, the study probed Striatin's influence on the NF-ᴋB expression, given albumin's role as a negative acute-phase protein. The results unveiled nucleoside diphosphate kinase (NdK) and parvalbumin (PV) as the prominent constituents within Striatin. Notably, NdK and PV exhibited the ability to disrupt hydrogen bonds with specific residues in both TGF-β and NF-κB complexes, thereby enhancing their flexibility, akin to the action of the FKBP12 complex (antagonist complex). In the in vitro experiments, Striatin demonstrated a dose and time-dependent inhibition of hypoalbuminemia, with peak efficacy observed at a concentration of 20 μg/mL. At this concentration, Striatin also suppressed NF-κB expression when co-incubated with lipopolysaccharides. While these findings suggest potential inhibitory effects of Striatin on TGF-β and NF-κB activities, they are preliminary and warrant further investigation. This study highlights Striatin's potential as a therapeutic agent for inflammation-related hypoalbuminemia, though additional research is needed to fully validate these results.

Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1.

De novo mutations in the synaptic GTPase activating protein (SynGAP) are associated with neurological disorders like intellectual disability, epilepsy, and autism. SynGAP is also implicated in Alzheimer's disease and cancer. Although pathogenic variants are highly penetrant in neurodevelopmental conditions, a substantial number of them are caused by missense mutations that are difficult to diagnose. Hence, in silico mutagenesis was performed for probing the missense effects within the N-terminal region of SynGAP structure. Through extensive molecular dynamics simulations, encompassing three 150-ns replicates for 211 variants, the impact of missense mutations on the protein fold was assessed. The effect of the mutations on the folding stability was also quantitatively assessed using free energy calculations. The mutations were categorized as potentially pathogenic or benign based on their structural impacts. Finally, the study introduces wild-type-SynGAP in complex with RasGTPase at the inner membrane, while considering the potential effects of mutations on these key interactions. This study provides structural perspective to the clinical assessment of SynGAP missense variants and lays the foundation for future structure-based drug discovery.

Predicting candidate miRNAs for targeting begomovirus to induce sequence-specific gene silencing in chilli plants.

The begomoviruses are the most economically damaging pathogens that pose a serious risk to India's chilli crop and have been associated with the chilli leaf curl disease (ChiLCD). Chilli cultivars infected with begomovirus have suffered significant decreases in biomass output, negatively impacting their economic characteristics. We used the C-mii tool to predict twenty plant miRNA families from SRA chilli transcriptome data (retrieved from the NCBI and GenBank databases). Five target prediction algorithms, i.e., C-mii, miRanda, psRNATarget, RNAhybrid, and RNA22, were applied to identify and evaluate chilli miRNAs (microRNAs) as potential therapeutic targets against ten begomoviruses that cause ChiLCD. In this study, the top five chilli miRNAs which were identified by all five algorithms were thoroughly examined. Moreover, we also noted strong complementarities between these miRNAs and the AC1 (REP), AC2 (TrAP) and betaC1 genes. Three computational approaches (miRanda, RNA22, and psRNATarget) identified the consensus hybridization site for CA-miR838 at locus 2052. The top predicted targets within ORFs were indicated by CA-miR2673 (a and b). Through Circos algorithm, we identified novel targets and create the miRNA-mRNA interaction network using the R program. Furthermore, free energy calculation of the miRNA-target duplex revealed that thermodynamic stability was optimal for miR838 and miR2673 (a and b). To the best of our knowledge, this was the first instance of miRNA being predicted from chilli transcriptome information that had not been reported in miRbase previously. Consequently, the anticipated biological results substantially assist in developing chilli plants resistant to ChiLCD.

Screening of DNMT3A inhibitors from phytochemicals using molecular docking and molecular dynamics simulation for their anti-cancer potential

ABSTRACT There is a consensus that epigenomic changes play a significant role in carcinogenesis. The effect of DNA methylation and its increased modulations on gene expression during carcinogenesis is significant for diagnostic and therapeutic purposes. Potential phytochemicals as anticancer approach modulators of epigenetic pathways are the focus of this investigation. Molecular docking studies were conducted to foretell how phytochemicals will interact with DNMT3A. As part of our effort to identify novel anti-cancer treatments, we examined a wide variety of phytochemicals from many chemical classes, including cannabinoids, carotenoids, chalcones, fatty acids, lignans, polysaccharides, saponins, steroids, and tannins. The docking scores for Dihydromyricetin are -10.207 kcal/mol, -9.313 kcal/mol for S-Adenosyl-L-methionine (SAM), and -8.757 kcal/mol for Zeylenol were determined to be superior than to phytochemical inhibitors against DNMT3A in the virtual screening method. Docking scores, hydrogen bond interactions, ADME characteristics, and DFT. Molecular Dynamics Simulation and free energy calculations demonstrated that these compounds bind stably to DNMT3A, potentially inhibiting its activity. Network Pharmacology suggested Dihydromyricetin's specific anticancer potential against cervical cancer. These findings provide insights into protein-Ligand interactions with Dnmt3A and highlights the need for In-vitro validation.

Absolute Binding Free Energies with OneOPES.

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.

Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations

Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations

Using a dual immunoinformatics and bioinformatics approach to design a novel and effective multi-epitope vaccine against human torovirus disease

Human Torovirus (HToV), a member of the Coronaviridae family, causes severe enteric diseases with no specific medication available. To develop novel preventative measures, we employed immunoinformatics techniques to design a multi-epitope-based subunit vaccine (HToV-MEV) triggering diverse immune responses. We selected non-allergenic, non-toxic, and antigenic epitopes from structural polyproteins, joined them with suitable linkers, and added an adjuvant 50S ribosomal L7/L12 peptide. The vaccine's solubility score of 0.903678 and physiochemical properties were found effective. Molecular dynamics simulations and free energy calculations revealed strong binding affinity for Toll-like receptor 3 (TLR-3), with a lowest energy score of −303.88, indicating high affinity. In-silico cloning and codon optimization showed significant production potential in E. coli. Immune simulations predicted a human immunological response. Our results are promising, but subsequent in vivo research is recommended. The HToV-MEV vaccine design demonstrates potential for preventing HToV-related diseases, and further investigation is warranted to explore its therapeutic applications.

Marine natural compounds as potential CBP bromodomain inhibitors for treating cancer: an in-silico approach using molecular docking, ADMET, molecular dynamics simulations and MM-PBSA binding free energy calculations.

The online version contains supplementary material available at 10.1007/s40203-024-00258-5.

Optimization of the interfacial properties between mechanically and electrochemically treated aluminium and nano-modified coatings for printed outdoor applications

The aim of this research was to analyze the interfacial properties between the surfaces of mechanically and electrochemically treated aluminium substrates and the printed coatings modified by the addition of silicon dioxide (SiO2) and titanium dioxide (TiO2) nanoparticles. The nanoparticles were added to the printing inks in various concentrations with the scope to optimize the interface properties of the prints for outdoor applications. The surface, as well as the structural and mechanical properties of the printed coatings were evaluated. Various methods were employed to measure and analyze the surface and interfacial properties of aluminium and coatings. Surface microphotographs and the roughness of the treated aluminium substrates were examined, the surface free energy of the aluminium and the coatings as well as the adhesion properties were calculated, and the thickness and optical density of the coatings were measured. Considering that the treated aluminium samples prepared for this research had quite textured and different surfaces, the contact angles used for the calculation of surface free energy were calculated with and without Wenzel model to obtain detailed information about the interactions between the surface of aluminium samples and the printed modified coatings. The results showed that the type of treatment of aluminium substrates as well as the modification of the printing inks have a significant influence on the adhesion and surface properties of the coatings considered. Furthermore, the results demonstrated that the produced nano-modified coatings can be used as printed coatings or as customized primers for applying various types of inks and coatings.

Computational Exploration of Isatin Derivatives for InhA Inhibition in Tuberculosis: Molecular Docking, MD Simulations and ADMET Insights.

Anti-tubercular drug discovery is a critical research area aimed at addressing the global health burden imposed by Mycobacterium tuberculosis. Nowadays, computational techniques have increased the likelihood of drug development compared to traditional, labor-intensive, and time-consuming drug design approaches. The pivotal goal of drug design is to identify compounds capable of selectively targeting protein, thereby disrupting its enzymatic activity. InhA, or NADH-dependent enoyl-acyl carrier protein reductase, stands at the forefront of targeted approaches in the battle against TB. Isatin derivatives have garnered interest for their diverse pharmacological activities. To identify novel isatin derivatives that could serve as potential chemical templates for anti-TB drug discovery by targeting InhA. The present work utilized various computational approaches, including molecular docking, binding free energy calculations, and conformational alignment studies to investigate the binding mode and interactions of carefully selected dataset of 88 isatin derivatives within InhA active site. Study also employed MD simulations of the most promising molecule to check the stability of the protein-ligand complex and in-silico ADMET profiling of the top compounds to predict their pharmacokinetic and toxicity properties. Results provided insights into the structural features contributing to InhA inhibition, assessing overall drug-like characteristics of isatin derivatives and identified compound 48 (BA= -10.4 kcal mol-1 ) with potential for further optimization. MD simulation analysis revealed that compound 48 binds firmly within the InhA protein, exhibiting minimal conformational fluctuations and enhanced stability. Considering the aforementioned, isatin derivatives represents a novel framework for creating targeted InhA inhibitors during anti-TB therapy. However, experimental validations and in-depth analyses are crucial to confirm efficacy and safety of these derivatives as potential InhA inhibitors for TB treatment.

Impact of Atomic Defects on Water Contact Angle of 2D Molybdenum Disulfide Surfaces.

Interfacial dynamics within nanofluidic systems are crucial for applications like water desalination and osmotic energy harvesting. Understanding these dynamics can inform the rational optimization of two-dimensional (2D) materials and devices for such applications. This study explores the wetting behavior of realistic 2D MoS2 surfaces incorporating vacancies and atomic steps, known as atomic defects. We employ a combined density functional theory (DFT) and molecular dynamics (MD) computational approach to elucidate the influence of atomic defects on the MoS2-water interface. DFT calculations are utilized to determine the charge distribution within MoS2. Subsequently, free energy calculations are obtained through MD simulations of the MoS2-water interface. Our findings underscore the importance of incorporating atomic defects into MoS2 surfaces for accurate water contact angle (WCA) predictions in nanofluidic simulations, particularly when using Abal et al. force field parameters. However, the force field developed by Liu et al. yielded more accurate results for pristine MoS2 surfaces. While these parameters provide reliable outcomes for pristine MoS2 surfaces, their application to surfaces with defects may lead to underestimation of WCA. This highlights the critical need for realistic surface representations in nanofluidic modeling to accurately capture the complex interactions between water and MoS2 materials.

Phytochemicals of Different Medicinal Herbs as Potential Inhibitors Against Dengue Serotype 2 Virus: A Computational Approach.

Dengue is one of the major mosquito-borne infectious diseases of the present century, reported to affect about 100-400 million people globally. The lack of effective therapeutic options has inspired several in vitro and in silico studies for the search of antivirals. Our previous study revealed the anti-dengue activity of different plant extracts from Plumeria alba, Bacopa monnieri, Vitex negundo, and Ancistrocladus heyneanus. Therefore, the current in silico study was designed to identify the phytochemicals present in the aforementioned plants, which are possibly responsible for the anti-dengue activity. Different plant databases as well as relevant literature were explored to find out the major compounds present in the above-stated plants followed by screening of the retrieved phytochemicals for the assessment of their binding affinity against different dengue viral proteins via molecular docking. The best poses of protein-ligand complexes obtained after molecular docking were selected for the calculation of binding free energy via MM-GBSA method. Based on the highest docking score and binding energy, six complexes were considered for further analysis. To analyze the stability of the complex, 100ns molecular dynamics (MD) simulations were carried out using Desmond module in the Schrodinger suite. The MD simulation analysis showed that four compounds viz. liriodendrin, bacopaside VII, isoorientin, and cynaroside exhibited stability with viral targets including the RdRp, NS3 helicase, and E protein indicating their potential as novel anti-dengue antivirals.

Vacancy formation free energy in concentrated alloys: Equilibrium vs. random sampling

Special Quasi-random Structures (SQSs) are often used to model disordered alloys in small simulation cells. Yet, SQS-based sampling yields defect formation energies that do not match the equilibrium values, for instance measured in atomic Monte Carlo simulations, due to the lack of chemical short-range order in random samples. In this paper, our approach to computing chemical potentials in alloys through random sampling techniques is extended to the computation of vacancy formation free energies. By rigorous thermodynamic derivation, we show that vacancy formation free energies computed from randomly sampled local chemical environments must be corrected to ensure its consistency between various available calculations. Indeed, irrespective of the choice of the species being replaced by a vacancy, we should arrive at the same value of the vacancy formation free energy. We propose a simple way to compute this correction term, and compare the outcome with equilibrium Monte Carlo results and with approximations found in the literature.

Exploring Protonation State, Ion Binding, and Photoactivated Channel Opening of an Anion Channelrhodopsin by Molecular Simulations.

Channelrhodopsins are light-gated ion channels with a retinal chromophore found in microbes and are widely used in optogenetics, a field of neuroscience that utilizes light to regulate neuronal activity. GtACR1, an anion conducting channelrhodopsin derived from Guillardia theta, has attracted attention for its application as a neuronal silencer in optogenetics because of its high conductivity and selectivity. However, atomistic mechanisms of channel photoactivation and ion conduction have not yet been elucidated. In the present study, we investigated the molecular characteristics of GtACR1 and its photoactivation processes by molecular simulations. The QM/MM RWFE-SCF method which combines highly accurate quantum chemistry calculations with long-time molecular dynamics (MD) simulations were used to model protein structures of the wild-type and mutants with different protonation states of key groups and to calculate absorption energies for verification of the models. The QM/MM modeling together with MD simulations of free-energy calculations favors protonation of a key counterion carboxyl group of Asp234 with a strong binding of a chloride ion in the extracellular pocket in the dark state. A channel open state was also successfully modeled by the QM/MM RWFE-SCF free-energy optimizations, providing atomistic insights into the channel activation mechanism.

Effect of Protein-Polarized Ligand Charges on Relative Protein Ligand Binding Affinities.

A major challenge in computer-aided drug design is predicting relative binding energies of different molecules to a target protein using fast and accurate free-energy calculation methods. Free-energy calculations are primarily computed by utilizing classical molecular dynamics simulations based on all-atom force fields (FF) to model the interactions in the system. The present standard classical all-atom FFs contain fixed partial charges on the atoms, and hence electrostatic interactions are modeled between them. The parametrization process to determine these partial charges usually relies on quantum mechanics or semiempirical calculations of the molecule in the gas phase or homogeneous water surrounding. These present standard parametrization schemes of the partial charges neglect, therefore, polarization effects from the protein surrounding. The absence of protein polarization effects can lead to significant errors in free-energy calculations in proteins. We present a parametrization scheme for the partial charges of ligands, named protein-induced polarization (PIP) charges, which account for the electrostatic polarization due to the protein surrounding. The scheme involves single-point quantum mechanics/molecular mechanics calculations of the ligand charges in the protein/water surrounding. Using PIP ligand partial charges, we have calculated the relative binding free energies (RBFEs) of well-studied protein-ligand systems. We show here that RBFEs computed with PIP charges are either significantly improved or at least comparable to those computed with nonpolarized standard GAFF charges. Overall, we present a simple-to-use parametrization scheme to include protein polarization in any type of binding free-energy calculations. The parametrization scheme increases the accuracy in RBFE calculations, while it does not add significant computation time to standard parametrization procedures.

Automated Adaptive Absolute Binding Free Energy Calculations.

Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a fully automated ABFE workflow based on the automated selection of λ windows, the ensemble-based detection of equilibration, and the adaptive allocation of sampling time based on inter-replicate statistics. We find that the automated selection of intermediate states with consistent overlap is rapid, robust, and simple to implement. Robust detection of equilibration is achieved with a paired t-test between the free energy estimates at initial and final portions of a an ensemble of runs. We determine reasonable default parameters for all algorithms and show that the full workflow produces equivalent results to a nonadaptive scheme over a variety of test systems, while often accelerating equilibration. Our complete workflow is implemented in the open-source package A3FE (https://github.com/michellab/a3fe).

Exploration of 4-tolyl-5-(p-tolyloxymethyl)-4H-1,2,4-triazole thioethers as potent 15-LOX inhibitors supported by in vitro, in silico, MD simulation and DNA binding studies

Inflammation is an intricate process exacerbated by the sequential release of lipid mediators during arachidonic acid metabolism through the LOX and COX pathways. The inhibition of enzymes accountable for the arachidonic acid metabolism is attributed to a synergistic anti-inflammatory impact with a broader spectrum of activity. The present study was concerned with the search for lead 15-LOX inhibitors. A new series of alkyl/aralkyl substituted 4-tolyl-5-(p-tolyloxymethyl)-4H-1,2,4-triazole (6a-h) was designed, synthesized, and characterized to determine their 15-LOX inhibitory potential. The analogue 3-(phenethylthio)-4-tolyl-5-(p-tolyloxymethyl)-4H-1,2,4-triazole (6h) was the most potent with IC50 2.73 ± 0.15 µM while 6g, 6d, 6f, 6c exhibited excellent inhibitory activities with IC50 values between 11.39 ± 0.13 and 14.63 ± 0.17 µM. All analogues maintained > 72 % blood mononuclear cells viability at 0.25 mM concentration during MTT assay. In silico ADME profiles predicted druggability potential. The molecular docking studies disclosed binding free energies of -9.78 to -7.94 kcal/mol and His373 and His378 were majorly involved in π-interactions in all molecules. Van der Waals interactions dominated hydrogen bonding in the stabilization of ligand-receptor complexes. MD simulations and free energy calculations revealed the significance of complexes in stabilizing the biomolecular system and helped in identifying the key active site residues that contributed towards the energetics. Studies against DNA dodecamer displayed minor groove binding and intercalation, suggesting a possible role in modulating DNA-related processes. The results approve the active analogues possessing promising druglike properties and work is in progress on the synthesis of more analogues in search for leads as 15-LOX inhibitors.

Improvements in Precision of Relative Binding Free Energy Calculations Afforded by the Alchemical Enhanced Sampling (ACES) Approach.

Accurate in silico predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.

Influential mechanism of water occurrence states of waste-activated sludge: Specifically focusing on the pore characteristics dominated by cation-organic interactions

The solid pore characteristics are commonly considered as the important influential factors on waste-activated sludge (WAS) dewaterability, and should be related to the cohesive force of bio-flocs dominated by cation-organic interactions at solid-water interface. This study aimed to establish an approach for regulating the solid pore structure of WAS by cationic regulation. The influential mechanism of WAS dewaterability was accordingly explored from the perspective of the pore characteristics dominated by cation-organic interactions. Primarily, with the gradient removal or addition of bivalent cations, the varying pore structure of WAS flocs was tracked by in-situ synchrotron X-ray computed microtomography imaging technique (CMT). The three-dimensional visual model was established to quantify the pore structure parameters of WAS flocs. Following the visualization analysis, the artificial intelligence means, the gradient-weighted class activation mapping (Grad CAM) of three-dimensional convolutional neural network (3D-CNN), was applied for the first time to explore the linkages among solid surface properties, solid pore structure, water occurrence states and sludge dewaterability. It was found that the number and volume of isolated pores jointly determined the mobility and the fractions of vicinal water and interstitial water (p-value ≤ 0.02); also, the decreasing polar or acid-based interfacial free energy with the cationic addition was accompanied with the decreasing isolated pore mean-volume (Pearson coefficient=-0.77, p-value < 0.01). These results indicated that the pore structure characteristics determined the water occurrence states, but the solid porosity strongly depended on the interfacial properties. Accordingly, the molecular docking was applied to explore the interfacial reaction mechanism between Ca2+/Mg2+ and solid compositions in terms of complexation sites, molecular dynamics and free energy calculations. As a result, how the cation-organic interactions affected the pore characteristics through solid surface modification could be clarified, which is expected to serve as theoretical foundation for the development of novel sludge conditioning technologies, i.e., more efforts should be devoted to increasing the dense degree of sludge particles through weakening the hydration repulsion of solid surface.

Computer-aided discovery of novel SmDHODH inhibitors for schistosomiasis therapy: Ligand-based drug design, molecular docking, molecular dynamic simulations, drug-likeness, and ADMET studies.

Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis.