Journal of Computer-Aided Molecular Design最新文献

{"title":"NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network","authors":"Yunyun Liang,&nbsp;Mengyi Cao","doi":"10.1007/s10822-026-00825-2","DOIUrl":"10.1007/s10822-026-00825-2","url":null,"abstract":"<div><p>Neuropeptides are multifunctional signaling molecules in the nervous system. By modulating synaptic transmission and integrating physiological systems, they influence a broad range of functions from pain perception to emotional regulation. Predicting neuropeptides can rapidly expand the library of potential therapeutic targets, thereby providing novel candidate molecules for drug development in areas such as analgesics, anti-anxiety medications, and weight-loss drugs. Traditional experimental methods are extremely time-consuming, labor-intensive, these promising alternative computational methods have emerged. In this study, a dual-branch deep learning architecture for neuropeptide prediction known as NeuroPred-GMC are built up based on gated dilated convolutional network with ESM-2 feature representation and multi-scale convolutional network with Prot-T5 feature representation. Dilated convolution exponentially enlarges the receptive field via increased dilation rates, gating mechanism enables dynamic, selective feature enhancement and noise suppression, and multi-scale convolution captures multi-level contextual information. On the independence test set, the accuracy of 93.24%, Sn of 93.69%, Sp of 92.79%, Pre of 92.86%, MCC of 0.8649 and the auROC of 0.9667 are obtained. The experimental results through cross-validation and independent test demonstrate that the proposed model has good robustness and generalizability, and can serve as a supplemental candidate predictor. The source datasets and codes can be freely available at https://github.com/yunyunliang88/NeuroPred-GMC.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147830060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico design and immunogenicity evaluation of a multi-epitope vaccine against EV-A71","authors":"Xiao Wang,&nbsp;Xiaowei Chen","doi":"10.1007/s10822-026-00822-5","DOIUrl":"10.1007/s10822-026-00822-5","url":null,"abstract":"<div><p>Enterovirus A71 (EV-A71), the primary causative agent of hand, foot, and mouth disease (HFMD), can cause severe neurological complications and even death, particularly in young children. Despite the availability of inactivated vaccines, their protective efficacy has been compromised due to frequent intra- and intertypic recombination events and ongoing mutations among circulating EV-A71 strains. To address this, we employed immunoinformatic approaches and identified conserved epitopes and constructed a multi-epitope vaccine (MEV) candidate against EV-A71. A total of 1,627 structural protein sequences from EV-A71 strains encompassing all major circulating subtypes were retrieved and aligned to generate a consensus sequence. With this consensus sequence, 11 conserved, antigenic, and non-allergenic epitopes capable of eliciting B-cell, T-cell, and interferon-gamma (IFN-γ) responses were identified. The constructed MEV demonstrated superior immunological potential with a high antigenicity score of 0.94 and was predicted to be non-allergenic and non-toxic. Structural characterization via AlphaFold 3 and 300 ns molecular dynamics (MD) simulations confirmed the formation of a stable β-strand framework. Molecular docking followed by trajectory-stabilized interaction analysis revealed that the MEV maintains a high-affinity and stable binding profile with Toll-like receptor 3 (TLR-3). To ensure optimal translational efficiency, the vaccine gene was codon-optimized with a GC content of 52.8%, and the protein was successfully expressed in a bacterial system. Collectively, this study provides a high-performance MEV candidate with robust structural stability and potent immunogenicity, offering a promising and cost-effective strategy for broad-spectrum protection against EV-A71.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147809172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights into the binding mechanism and structural requirements of LPAR2 antagonists as antifibrotic agents based on homology modeling, molecular docking, prediction of membrane permeability and 3D QSAR","authors":"Ying Zhang,&nbsp;Guifu Xu,&nbsp;Puhua Wu","doi":"10.1007/s10822-026-00820-7","DOIUrl":"10.1007/s10822-026-00820-7","url":null,"abstract":"<div><p>Lysophosphatidic acid receptor 2 (LPAR2), a G protein-coupled receptor, has been implicated in the progression of fibrosis and is therefore a promising novel drug target for the treatment of fibrosis and related diseases. In this paper, a reliable homology model of LPAR2 was obtained by using three templates (PDB IDs: 4Z34, 7TD0, and 7VIE) and evaluations. A new binding site for a series of selective LPAR2 inhibitors were identified through molecular docking with the reference compound <b>50</b>. Subsequently, a three-dimensional quantitative structure-activity relationship (3D QSAR) analysis was conducted on a series of N-sulfonyl heterocyclic antagonists of LPAR2. The derived optimal CoMFA model (q² = 0.792, r² = 0.999, <span>( r_{{pred}}^{2} )</span> = 0.998, <span>( r_{{m(over{text{ }}all)}}^{2} )</span>  = 0.978) and CoMSIA model (q² = 0.713, r² = 0.996, <span>( r_{{pred}}^{2} )</span> = 0.978, <span>( r_{{m(over{text{ }}all)}}^{2} )</span> = 0.958) demonstrated strong statistical robustness and high external predictability. The 3D contour maps generated from these models were analyzed and compared with the binding mode of the reference compound. This provided insights into the structural requirements of these LPAR2-selective inhibitors. Furthermore, the predictive capability of these models was validated by accurately predicting the antagonistic activities of other types of LPAR2-selective inhibitors (CoMFA-SE, <span>( r_{{pred}}^{2} )</span> = 0.862; CoMSIA-SEHDA, <span>( r_{{pred}}^{2} )</span>  = 0.934), confirming the robustness of the optimal 3D QSAR models. The new binding site and the optimal 3D QSAR models will be helpful to design novel molecules and predict their inhibitory activity against LPAR2.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147809153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric deep reinforcement learning with hierarchical variational autoencoders for de novo drug design and activity optimization","authors":"Dileep Kumar Murala","doi":"10.1007/s10822-026-00812-7","DOIUrl":"10.1007/s10822-026-00812-7","url":null,"abstract":"<div><p>Traditional drug discovery is a resource-intensive process with high attrition rates and the huge difficulty of working with a chemical space that is thought to include <span>(10^{60})</span> molecules. Even though computational chemistry has come a long way, traditional generative models still use string-based representations like SMILES, which have trouble capturing intricate three-dimensional spatial interactions and often make structures that aren’t real. Moreover, current reinforcement learning methodologies frequently do not achieve an equilibrium between molecular diversity and high-affinity biological activity. To overcome these constraints, this research introduces an innovative integrated framework that merges Geometric Multi-Discrete Soft Actor-Critic (Geom-SAC) and Multi-stage Variational Autoencoders (MS-VAE) to improve de novo molecular creation and activity optimisation. The main new idea is the combination of geometric deep learning, which enforces physical atomic restrictions, and a hierarchical VAE architecture, which organises the latent space into manageable structural steps from scaffold formation to functional group optimisation. We also use a Non-Covalent Interaction-Aware (NCIA) graph neural network in our method to improve protein-ligand affinity predictions by simulating complex intermolecular forces. Experimental results on benchmark datasets, such as ZINC250k and PDBbind, show that the proposed framework improves binding affinity scores by 15% and the Valid-Unique-Novel (VUN) molecule ratio by 20% compared to the best existing methods. Also, adding a security layer based on blockchain technology makes sure that data is secure and can be tracked. This all-encompassing method provides a strong, highly accurate answer for next-generation AI-driven pharmacology. It greatly narrows the gap between computational design and experimental validation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147809106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resolving the ambiguous binding site of quercetin at the calcineurin subunit junction using funnel metadynamics with deep learning collective variables","authors":"Jason S. E. Loo,&nbsp;Mei Qian Yau","doi":"10.1007/s10822-026-00817-2","DOIUrl":"10.1007/s10822-026-00817-2","url":null,"abstract":"<div><p>Calcineurin represents a prominent target for immunosuppressive drugs, where conventional macrocyclic inhibitors utilize an immunophilin-dependent mechanism for their inhibition but are consequently hindered by adverse effects and variable pharmacokinetics. The flavonoid quercetin has been shown to inhibit calcineurin in a non-competitive, immunophilin-independent manner, but its exact binding site remains ambiguous at the junction between calcineurin subunits A and B, with the open solvent-exposed nature of this region proving challenging to model. In this study, we employ funnel metadynamics with collective variables derived from a deep learning model to identify the binding site of quercetin. A selective strategy using multiple simulations with stricter funnel definitions and a smaller number of carefully chosen descriptors for model training proved more effective than a broad-based approach. These simulations were able to effectively distinguish the binding site of quercetin from three experimentally suggested sites, with a calculated free energy of binding of −8.36 ± 0.60 kcal/mol showing excellent agreement with experiment. Ligand-tryptophan distances similarly corroborated measurements from FRET assays with a <i>r</i>² of 0.85. The corresponding binding pose showed that quercetin inserts itself into a channel between Arg122, Gly123, Tyr124 on one side and Phe160, Thr161, and Asn345 on the other, stabilizing its binding through a network of hydrogen bonds. The findings of this study provide insights into the modelling of challenging binding sites and ligands using deep learning driven metadynamics simulations and provide the foundations for rational development of immunophilin-independent inhibitors of calcineurin.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147759306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic study of parameter sharing strategies in multi-task learning for drug synergy and sensitivity prediction","authors":"C. A. Hafsath,&nbsp;A. S. Jereesh","doi":"10.1007/s10822-026-00808-3","DOIUrl":"10.1007/s10822-026-00808-3","url":null,"abstract":"<div>\u0000 \u0000 <p>Multi-task learning (MTL) is a useful approach for modeling related prediction tasks by sharing representations while allowing each task to keep its unique features. In computational drug discovery, drug synergy and drug sensitivity predictions are closely linked, but it is still unclear where and how to share model parameters between these tasks. In this study, we introduce a multi-task learning framework that predicts both drug synergy and drug sensitivity, with a main goal of improving synergy prediction by using sensitivity prediction as a supporting task. Our model uses molecular descriptors and drug induced gene expression signatures to describe pharmaceuticals, while untreated cancer cell lines are described by their baseline gene expression profiles. We use a mutual attention mechanism to capture complex interactions between drugs and cells. With this shared feature extraction base, we systematically test different parameter sharing strategies, looking at both hard and soft sharing at various network depths. Our experiments show that the success of parameter sharing depends on both the sharing strategy and the network layer where sharing happens. Hard parameter sharing works best at deeper layers, while soft parameter sharing allows for more stable and flexible knowledge transfer between tasks. Among the soft sharing methods we tested, dense sharing based on Neural Discriminative Dimensionality Reduction (NDDR) consistently outperforms cross stitch and sluice networks, especially when used in early network layers. Extending dense sharing to multiple layers further improves shared representations and leads to better performance. An updated NDDR version with nonlinear bottleneck transformations and residual connections achieves the highest overall performance. In summary, this work gives clear insight about where and how information should be shared in multi-task learning for drug synergy and sensitivity prediction. Our results show that both the sharing level and the way sharing is done strongly affect performance. Multi-level and depth-aware sharing strategies lead to better predictions. These findings give practical guidance for building effective multi-task models in computational drug discovery.</p>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design, purification, and biophysical characterization of a multi-epitope vaccine construct made from outer membrane proteins of Pseudomonas aeruginosa","authors":"Yogeshwar Devarakonda,&nbsp;Syed Mohammad Omer Ali,&nbsp;Juan Edwin James,&nbsp;Abhinav Cheruvu Manikantha Sai,&nbsp;Sakshi Batra,&nbsp;Kirtimaan Syal","doi":"10.1007/s10822-026-00813-6","DOIUrl":"10.1007/s10822-026-00813-6","url":null,"abstract":"<div><p><i>Pseudomonas aeruginosa</i> is increasingly becoming resistant to multiple drugs and is held responsible for high rate of mortality and morbidity across the globe. This study aims to examine proteins from outer membrane such as OprE, OprF, OprC, and OprG, for the multi-epitope vaccine design. In this article, the prediction of epitopes for helper T-cells, cytotoxic T-cells, and B-cell epitopes was carried out by the application of various immune-informatics tools. All the predicted epitopes were aligned and assembled into a peptide sequence along with linkers and adjuvant sequences. Further, secondary structure and three-dimensional structure were predicted for the multiepitope construct. The vaccine construct designs were evaluated and validated for allergenicity, toxicity, and antigenicity. The validation of the predicted structure was carried out by determining its physiochemical properties by the application of ProtParam. Protein docking and molecular-dynamic simulation confirmed strong and stable interaction between the vaccine construct and Toll-like receptor-4. The vaccine construct was cloned into pET-29b vector and expressed in <i>Escherichia coli</i>. The designed multiepitope vaccine construct was overexpressed and purified by the application of Ni-NTA affinity chromatography and subjected to SDS-PAGE analysis. The circular dichroism spectroscopy analysis revealed it to be stable and structured. The hemolysis assay demonstrated minimal RBC toxicity suggesting that it was safe to use. The designed vaccine construct could activate both humoral and cellular immune responses as demonstrated by the advanced immunoinformatic approach making it a promising vaccine construct for protection against <i>P. aeruginosa</i>.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147759217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural compounds from Fernandoa adenophylla (Wall. ex G.Don) Steenis as beta-secretase 1 and monoamine oxidase-b inhibitors: in vitro and computational evidence","authors":"Zubaida Saad,&nbsp;Saima Naz,&nbsp;Abdur Rauf,&nbsp;Rahaf Ajaj,&nbsp; Marryum,&nbsp;Ayesha Tahir,&nbsp;Umer Rashid,&nbsp;Zuneera Akram,&nbsp;Zafar Ali Shah,&nbsp;Alessandra Gianoncelli,&nbsp;Giovanni Ribaudo","doi":"10.1007/s10822-026-00814-5","DOIUrl":"10.1007/s10822-026-00814-5","url":null,"abstract":"<div><p>This study investigates the inhibitory effects of 5 bioactive compounds isolated from <i>Fernandoa adenophylla</i> on two key enzymes involved in neurodegenerative diseases, Beta-secretase 1 (BACE-1) and monoamine oxidase-B (MAO-B). The compounds, namely lapachol (<b>1</b>), alpha-lapachone (<b>2</b>), peshawaraquinone (<b>3</b>), dehydro-alpha-lapachone (<b>4</b>), and the indanone derivative methyl 1,2-dihydroxy-2-(3-methylbut-2-enyl)-3-oxoindene-1-carboxylate (<b>5</b>) were tested in vitro and investigated by means of computational tools. Lapachol (<b>1</b>) resulted to be the most effective BACE-1 inhibitor of the set, while peshawaraquinone (<b>3</b>) strongly inhibited MAO-B. Enzyme kinetic analyses were carried out, and the inhibitory mechanisms were also elucidated. Molecular docking studies showed that the compounds target key residues in the active sites of the enzymes and density functional theory (DFT) investigation suggested a higher reactivity of lapachol (<b>1</b>) towards electron transfer. Overall, the findings support the potential role of these natural compounds as BACE-1 and MAO-B inhibitors.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><img></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying conformational diversity in protein–ligand ensembles for structure-based virtual screening","authors":"Pei−Kun Yang","doi":"10.1007/s10822-026-00811-8","DOIUrl":"10.1007/s10822-026-00811-8","url":null,"abstract":"<div><p>Structure-based virtual screening (SBVS) requires accurate representations of protein and ligand conformational states, since both components can adopt diverse geometries that influence binding energetics. Using 79 HIV-1 protease-ligand complexes, we quantify how conformational heterogeneity and ensemble clustering shape the evaluation of interaction energies by comparing native complexes, nonnative pairings, protein ensembles derived from apo MD, and ligand ensembles sampled in solution. Native complexes consistently yield favorable interactions, whereas nonnative pairings are rarely favorable and often highly unfavorable, indicating that many failures in screening that uses a single structure arise from protein–ligand geometric mismatch rather than from the scoring function alone. We further show that structural reduction of protein and ligand ensembles decreases the recovery of favorable interaction states, and that simultaneous reduction of both ensembles constrains the availability of complementary structural pairs. Because SBVS is often limited by computational cost, receptor ensembles are typically restricted to a small number of conformations. Here, we quantify the sampling scale and the level of ensemble reduction required to retain binding-compatible geometries and stable interaction-energy trends, providing practical guidance for ensemble-based screening.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico design, chemistry and biological activity of thiazole-based azetidinone Schiff bases as potential anti-Alzheimer agents","authors":"Sindhu Thattil Johny,&nbsp;Jainey Puthenveetil James,&nbsp;Zakiya Fathima Cherangai,&nbsp;Rajalakshimi Vasudevan,&nbsp;Venugopal Shri Vidya,&nbsp;Sheshagiri Dixit,&nbsp;Saravanan Parameswaran","doi":"10.1007/s10822-026-00809-2","DOIUrl":"10.1007/s10822-026-00809-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Neurodegenerative disorders remain a critical challenge due to their complex pathology and limited therapeutic options. In this context, we report the computer-aided design, synthesis and comprehensive evaluation of novel heterocycles, rationally developed based on the pharmacophoric features of the clinically used acetylcholinesterase inhibitor Donepezil for Alzheimer’s disease (AD). These compounds were meticulously characterised using advanced spectroscopic techniques such as FTIR, HRMS, ¹³C NMR and ¹H NMR. Molecular docking studies were conducted against acetylcholinesterase (AChE), human peroxiredoxin and tyrosinase enzymes, revealing favourable binding interactions and predicted affinities. The synthesized compound 6c showed docking score − 8.82 kcal/mol and formed significant interactions with key active-site residues with AChE. In vitro, Ellman's assay confirmed potent enzyme inhibition of compound 6c IC₅₀ (5.54 ± 1.12 µM), highlighting its therapeutic promise. The MTT assay was conducted to assess the cell viability of the synthesised compounds, and cellular studies in SH-SY5Y neuroblastoma cells demonstrated AChE inhibition (55.12%), indicating potential neuroprotective effects. In vivo cognitive assessments in wistar rats using Y-Maze and Novel object recognition paradigms exhibited improvements in memory and learning, further supported by biochemical analyses showing enhanced activities of AChE, catalase, and superoxide dismutase (SOD). Histopathological examination of brain tissues corroborated the neuroprotective efficacy of these thiazole-based azetidinone Schiff bases. Collectively, this multidisciplinary approach establishes the thiazole azetidinone Schiff base derivative <b>6c</b> as a prospective applicant for the advancement of novel therapeutics targeting AD.</p>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}