Computational Biology and Chemistry最新文献

{"title":"Design and computational characterization of arginine-functionalized ZIF-8 as a pH-responsive oral insulin carrier","authors":"Ali Muneer Abdulrahman ,&nbsp;Dina Hadi Abdullah Alaqebe ,&nbsp;Zainab Abdullah kareem ,&nbsp;Eman Mohammed jasim ,&nbsp;Mohammed Mahmood Abdullah ,&nbsp;Alaa Hamid Faisal ,&nbsp;Mustafa M. Kadhim","doi":"10.1016/j.compbiolchem.2025.108711","DOIUrl":"10.1016/j.compbiolchem.2025.108711","url":null,"abstract":"<div><div>This study presents a computational framework to evaluate arginine-modified ZIF-8 (ZIF-8@Arg) as a potential oral insulin delivery system. In contrast to unmodified metal–organic frameworks, the inclusion of arginine residues introduces guanidinium groups that enhance insulin interaction through electrostatic and hydrogen bonding effects. A combination of multiscale modeling techniques, including density functional theory (DFT), time-dependent DFT, molecular dynamics simulations, and topological analyses Atoms in Molecules (AIM) and Non-Covalent Interaction (NCI), was employed to characterize the molecular interface and environmental responsiveness. The results indicate improved binding stability at the ZIF-8@Arg–insulin interface, as well as pH-dependent structural adaptability, with swelling was observed under basic conditions. The activation energy for insulin release was calculated to be 15.39 kcal/mol. Solvation energy and partition coefficient (logP) analyses suggest favorable permeability characteristics. In silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiling indicates low predicted toxicity and compatibility with oral administration. Overall, the findings support further investigation of ZIF-8@Arg as a functional MOF-based carrier with tunable release behavior and acceptable pharmacokinetic properties for oral peptide delivery.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108711"},"PeriodicalIF":3.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated computational bioprospection of flavonoids as modulators of Mycobacterium tuberculosis decaprenylphosphoryl-β-d-ribose-2′-epimerase 1","authors":"Kakudji Kisimba ,&nbsp;Rukayat Abiola Abdulsalam ,&nbsp;Elliasu Y. Salifu ,&nbsp;Saheed Sabiu ,&nbsp;Mbuso Faya","doi":"10.1016/j.compbiolchem.2025.108719","DOIUrl":"10.1016/j.compbiolchem.2025.108719","url":null,"abstract":"<div><div>Tuberculosis (TB) remains a significant global health threat, claiming millions of lives annually despite being preventable. The emergence of drug-resistant strains, including extensively drug-resistant TB (XDR-TB) and multidrug-resistant TB (MDR-TB), severely limits conventional treatment options. Furthermore, commonly used TB medications like isoniazid (INH) and rifampicin (RIF) are associated with adverse side effects. Consequently, researchers increasingly explore natural products as potential sources for novel anti-TB therapeutics. This study investigated the inhibitory potential of 103 flavonoid compounds with documented antimycobacterial activity against TB. Focusing on decaprenylphosphoryl-β-D-ribose 2′-epimerase 1 (DprE1) as a druggable target, we employed molecular docking, pharmacokinetic evaluation, and 200-ns molecular dynamics simulations to assess stability and energy refinement. Our results showed that the top five compounds exhibited more favourable binding free energy values against DprE1 than the standard, PBTZ169. Notably, cycloartobiloxanthone demonstrated a binding free energy of −63.67 kcal/mol, surpassing PBTZ169 (-37.78 kcal/mol). Structural analysis revealed that cycloartobiloxanthone stabilised the protein and formed additional interactions without compromising its integrity. These findings suggest a potential structural mechanism for the inhibitory action of cycloartobiloxanthone against <em>Mycobacterium tuberculosis</em> DprE1. While this study highlights the potential of cycloartobiloxanthone as a lead compound, further validation through <em>in vivo</em> and <em>in vitro</em> studies is recommended.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108719"},"PeriodicalIF":3.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 strategies for discovering and analyzing new molecules based on isocycloseram: Virtual screening, docking, molecular dynamics, MM/PBSA, and GABA receptor interactions","authors":"Taináh M.R. Santos ,&nbsp;Artur G. Nogueira ,&nbsp;Antônio P.L. Mesquita ,&nbsp;Alexandre A. De Castro ,&nbsp;Teodorico C. Ramalho","doi":"10.1016/j.compbiolchem.2025.108710","DOIUrl":"10.1016/j.compbiolchem.2025.108710","url":null,"abstract":"<div><div>The agricultural insecticide market is becoming increasingly competitive. With the launch of <strong>PLINAZOLIN</strong>® <strong>Technology</strong>, significant attention has been given to its active ingredient, Isocycloseram, which is effective across more than 40 plant crops. However, there are already reports of resistance development to this insecticide. Given this scenario, the search for new molecules that act similarly to Isocycloseram in controlling various pests is essential. However, the discovery and development of new molecules can take years and require substantial financial investment. A useful and efficient alternative is applying <em>in silico</em> methods to accelerate the discovery of new active ingredients, making the development process shorter and more cost-effective. In this context, this study aimed to apply <em>in silico</em> methods to identify new active ingredients with pharmacophoric groups similar to those of Isocycloseram, given its relevant applications in various plant crops. To achieve this, virtual screening was performed on eight molecular databases, comprising over 215 million compounds, to identify new molecules of interest. Subsequently, multiple filtering steps were applied to ensure that only the best-ranked compounds were selected. Since the three-dimensional structure of the GABA receptor, the molecular target of Isocycloseram, is unavailable, homology modeling was conducted, along with validation of the generated model. Additionally, <em>docking</em> simulations, molecular dynamics, MM/PBSA calculations, and various analyses were performed. Overall, this study presents a novel approach using <em>in silico</em> methods to identify desirable active ingredients and provides two new molecules that could enhance market competition against Isocycloseram, thereby expanding the portfolio of agricultural insecticides.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108710"},"PeriodicalIF":3.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing & screening of siRNA molecules for silencing the impact of the VEGF gene in cancer cells","authors":"Mohd Nazam Ansari ,&nbsp;Abdulaziz S. Saeedan ,&nbsp;Sara A. Aldossary","doi":"10.1016/j.compbiolchem.2025.108708","DOIUrl":"10.1016/j.compbiolchem.2025.108708","url":null,"abstract":"<div><div>Cancer is a complex disease characterized by uncontrolled cell proliferation and metastasis, with breast cancer remaining a leading cause of mortality among women worldwide. Hypoxia-inducible factor (HIF) and vascular endothelial growth factor (VEGF) are key mediators of angiogenesis, sustaining tumor growth and progression. RNA interference (RNAi) has emerged as a promising gene-silencing strategy for targeted cancer therapy. In this study, we designed small interfering RNAs (siRNAs) against VEGF mRNA using computational approaches. VEGF gene sequences were retrieved from NCBI, and siRNAs were designed using siDirect v2.0 and i-Score Designer. Candidate siRNAs were screened based on GC content (30–52 %), secondary structure, and thermodynamic stability. Hybridization energy analysis revealed favourable binding to VEGF mRNA, ranging from –31.1 to –37.3 kcal/mol. Molecular docking with h-Argonaute-2 (h-Ago2) yielded docking scores between –330 and –351 kcal/mol, indicating efficient RISC loading. Molecular dynamics (MD) simulations further demonstrated stable siRNA–Ago2 complexes, with RMSD values stabilizing around 2.1–2.6 Å and RMSF fluctuations primarily localized to the PAZ and MID domains. These findings confirm strong binding affinity, structural stability, and specificity of the designed siRNAs. Overall, our results suggest that RNAi-based silencing of VEGF holds significant potential as a therapeutic strategy for inhibiting angiogenesis in breast cancer.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108708"},"PeriodicalIF":3.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 exploration of bioactive compounds targeting the CrtM to impede Staphylococcus aureus drug resistance: Pigment inhibitors","authors":"Sharon Munagalasetty,&nbsp;Samir Khan,&nbsp;Vitthal Kale,&nbsp;Vasundhra Bhandari","doi":"10.1016/j.compbiolchem.2025.108707","DOIUrl":"10.1016/j.compbiolchem.2025.108707","url":null,"abstract":"<div><div>The World Health Organization has designated the Methicillin-resistant S<em>taphylococcus aureus</em> (MRSA) and its variants as high-priority threats owing to their enhanced virulence and pathogenic potential. Staphyloxanthin (STX), a prominent virulence factor of <em>S. aureus,</em> plays a dual role: it shields the bacterium from oxidative stress generated by the host immune response and preserves the cell membrane integrity. Dehydrosqualene synthase (CrtM), a prenyl transferase, is essential for catalyzing the first step of STX biosynthesis. In this study, we evaluated 144,000 compounds, including anticancer agents, inhibitors and approved drugs, and 3D bioactive molecules to inhibit the CrtM using computational approaches. Virtual screening was performed on the prepared compound library, followed by relative binding free energy calculations based on MM/GBSA for hit compounds and 100 ns molecular dynamics (MD) simulations for top 3 hit candidates. BPH-652, a known CrtM inhibitor, was used as the reference. Our results revealed that Cmpd1 and Cmpd2 exhibit docking scores of −13.113 kcal/mol and −13.015 kcal/mol, respectively compared to BPH-652(-10.74 kcal/mol) against the CrtM. The stability was further confirmed with relative binding free energies of −57.70 kcal/mol for BPH-652, and −104.74 and −113.20 kcal/mol for Cmpd1 and Cmpd2, respectively. MD simulations demonstrated stable behavior of Cmpd1 and Cmpd2 inside active site of CrtM with minimal fluctuations, the binding energy calculated from MD trajectories also support strong affinity of these compounds. Their favorable ADME properties suggest the potential for further validation in <em>in vitro</em> and <em>in vivo</em> levels.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108707"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of protein thermostability trends based on the self-attention mechanism driven sparse convolutional network","authors":"Pei Xu ,&nbsp;Kai Zhong ,&nbsp;Honghua Ge ,&nbsp;Xiaoping Song ,&nbsp;Weihua Wang","doi":"10.1016/j.compbiolchem.2025.108693","DOIUrl":"10.1016/j.compbiolchem.2025.108693","url":null,"abstract":"<div><div>Artificial intelligence (AI)-assisted thermostability prediction of proteins can significantly alleviate the burden of mutation screening, thereby enhancing the efficiency of protein engineering. To further improve prediction accuracy and shorten the development cycle of new proteins, we integrate protein sequences, mutation relationships, and physicochemical properties for encoding, introducing the innovative Sparse Convolutional Network driven by the self-attention mechanism, named SCSAddG. Experimental results demonstrate that SCSAddG achieves a prediction accuracy of 0.868, a precision of 0.710, a recall of 0.606, an F1 score of 0.653, and an area under the Receiver Operating Characteristic (AUROC) of 0.825 in the general dataset S2648. Compared to traditional Convolutional Neural Networks (CNN), SCSAddG exhibits slightly higher prediction accuracy and outperforms the Rosetta bioinformatics simulation software 12% in terms of accuracy. Furthermore, in the experimental transglutaminase dataset, SCSAddG exhibits significantly better prediction accuracy compared to CNN (0.744 vs. 0.667), achieving a precision of 1.000. The results of wet laboratory experiments are consistent with the model predictions. In the 5-fold cross-validation, the SCSAddG model outperformed the CNN across multiple evaluation metrics, demonstrating its superior predictive performance and robust reliability. These results indicate that SCSAddG can effectively evaluate the trends in protein thermostability and serve as a valuable tool to guide protein thermostability engineering.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108693"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches to predict drug resistance in tuberculosis","authors":"A.T. Subalakshmi,&nbsp;Arundhati Mahesh","doi":"10.1016/j.compbiolchem.2025.108705","DOIUrl":"10.1016/j.compbiolchem.2025.108705","url":null,"abstract":"<div><div>Tuberculosis (TB) remains a global health crisis, with 10.8 million cases and 1.25 million deaths in 2023. The rise of drug-resistant TB has complicated treatment, while traditional diagnostic methods face limitations in speed, cost, and accuracy. This study explores machine learning (ML) models to predict drug resistance from genomic variants, offering a faster and more comprehensive solution.</div><div>We compiled a comprehensive dataset of variations and mutations associated with resistance phenotypes from databases such as TBDReaMDB, GMTV, WHO, and CARD. For each mutation, both sequence-based features (e.g., physicochemical property changes, Provean scores) and structure-based features (e.g., hydrophobicity, flexibility, accessible surface area) were derived. Ensemble ML models (Stacking, Bagging and Voting Classifiers) were evaluated for their ability to predict resistance to key anti-TB drugs: Fluoroquinolones, Rifampicin, Isoniazid, and Pyrazinamide.</div><div>Results achieved indicated that the model behaved differently on six TB resistance genes (gyrA, gyrB, inhA, katG, rpoB, pncA), with accuracy varying from 66 % (gyrA Stacking) to 91.37% (pncA Voting) and ROC scores varying from 0.69 (gyrA Bagging) to 0.92 (pncA Stacking). The Bagging model performed best for gyrA, gyrB and rpoB with strong classification, while the Stacking classifier performed well for inhA. Voting classifier proved to be the top-performing classifier for katG and pncA gene. The top-performing model for both genes was chosen, emphasizing a gene-specific strategy to maximize resistance prediction.</div><div>This study demonstrates that gene-specific ensemble models, supported by a comprehensive feature set, can provide valuable predictions of drug resistance in <em>M. tuberculosis</em>. While promising, the findings remain a proof-of-concept and require further validation on larger and more diverse clinical datasets before clinical application.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108705"},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers","authors":"Binsheng He ,&nbsp;Yanyu Ma ,&nbsp;Kun Wang ,&nbsp;Pingping Bing ,&nbsp;Lei Ji ,&nbsp;Geng Tian ,&nbsp;Haiyan Liu ,&nbsp;Pingan He ,&nbsp;Jialiang Yang","doi":"10.1016/j.compbiolchem.2025.108703","DOIUrl":"10.1016/j.compbiolchem.2025.108703","url":null,"abstract":"<div><div>Accurate prognostic stratification is essential for optimizing postoperative therapeutic strategies in oncology. While deep learning approaches have shown promise for survival prediction through unimodal analyses of histopathological images, transcriptomic profiles, and microbial signatures, their clinical utility remains limited due to fragmented biological insights. In this study, we introduce HMTsurv, a multimodal survival prediction framework that integrates digital histopathology, host transcriptomics, and tumor-associated microbiome features. Utilizing multi-omics datasets from four major malignancies—colorectal, gastric, hepatocellular, and breast cancers—our model exhibited superior prognostic accuracy (c-index: 0.68–0.72) when compared to single-modality benchmarks, as validated through rigorous cross-validation methods. Notably, our model achieved robust risk stratification (log-rank p &lt; 0.001 across all cohorts) as demonstrated by Kaplan-Meier analysis, effectively distinguishing patients into distinct survival trajectories. Systematic examination of multimodal signatures identified 14 pan-cancer survival biomarkers, including MAGE family genes, which were consistently upregulated in high-risk subgroups. Additionally, we elucidated distinct histopathological patterns, dysregulated microbial communities, and altered gene-microbiota co-expression networks that were predictive of adverse outcomes. This study not only establishes a generalizable multimodal architecture for cancer prognosis but also elucidates the intricate interactions among histological, molecular, and ecological determinants of survival, providing a clinically actionable framework for precision oncology.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108703"},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq","authors":"Duaa Mohammad Alawad ,&nbsp;Ataur Katebi ,&nbsp;Md Tamjidul Hoque","doi":"10.1016/j.compbiolchem.2025.108702","DOIUrl":"10.1016/j.compbiolchem.2025.108702","url":null,"abstract":"<div><div>Gene regulatory networks (GRNs) govern gene expression and cellular identity, but accurately inferring their structure from high-dimensional single-cell RNA sequencing (scRNA-seq) data remains a major challenge. Here, we present EnsembleRegNet, a deep learning framework that infers transcription factor (TF)–target gene relationships by integrating an ensemble encoder-decoder and multilayer perceptron (MLP) architecture. EnsembleRegNet utilizes Hodges-Lehmann estimator (HLE)-based binarization, case-deletion analysis, motif enrichment using RcisTarget, and regulon activity scoring with AUCell to enhance both robustness and biological interpretability. Extensive evaluations across simulated and real scRNA-seq datasets demonstrate that EnsembleRegNet outperforms existing GRN inference methods, including SCENIC and SIGNET, in both clustering performance and regulatory accuracy. By uncovering cell-type-specific regulatory modules and enhancing interpretability, EnsembleRegNet offers a scalable and biologically grounded framework for exploring transcriptional regulation. Its demonstrated performance establishes a new benchmark for GRN inference and highlights its promise for applications in disease modeling, biomarker discovery, and cellular reprogramming.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108702"},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing and computational studies of Novel 5-Fluorouracil hybrids as thymidylate synthase inhibitors for targeting non small cell lung cancer","authors":"Dishank Purandare ,&nbsp;Prajakta Adsule ,&nbsp;Rahul Jawarkar ,&nbsp;Aryaa Nigade ,&nbsp;Satish Polshettiwar","doi":"10.1016/j.compbiolchem.2025.108697","DOIUrl":"10.1016/j.compbiolchem.2025.108697","url":null,"abstract":"<div><div>Cancer is a multifactorial disease characterized by uncontrolled cellular proliferation and impaired regulatory mechanisms, and among its diverse forms, NSCLC remains one of the most prevalent and lethal malignancies worldwide. To address NSCLC, scientists are placing great emphasis on drugs that can reduce cell resistance, improve potency or prevent DNA alterations. Recent studies on TS inhibitors have demonstrated the potential of effective management of NSCLC with minimal adverse reactions. Therefore, there is a demand for more advanced and efficient anti-cancer medications targeting TS inhibitors, as promised in the development of new anti-cancer drugs that have reduced or no adverse effects. The objectives of our recent study were to design and evaluate 5-FU-based hybrids that can inhibit TS in comparison to already known inhibitors such as Raltitrexed. We designed and performed a detailed computational study on 5-FU hybrids, intending to use them as possible inhibitors of TS. After performing various studies on 12 molecules designed, it was found that compounds C04, C08, and C12 presented better results when compared with the standard drug (Raltitrexed). Not only that, but all the designed compounds showed higher stability when compared to the standard drug. Compounds C04, C08 and C12 were chosen for additional screening for molecular studies like Free landscape energy, Principal component analysis, free binding energy study and MD Simulation. Docking investigation utilised the crystal structure of the thymidylate synthase inhibitor enzyme (PDB ID: 6ZXO). Further, MD simulations and QSAR Studies were performed, and satisfactory results were obtained, suggesting future potential drugs for the treatment of NSCLC.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108697"},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}