Journal of molecular graphics & modelling最新文献

{"title":"Machine Learning–Driven discovery of mushroom-derived inhibitors targeting InhA of Mycobacterium tuberculosis: An integrated QSAR, molecular docking and molecular dynamic simulation approach","authors":"Karma Wangchuk ,&nbsp;Mudassar Fareed Awan ,&nbsp;Syeda Nazish Sohaib ,&nbsp;Abdul Basit ,&nbsp;Biniyam Prince Danan ,&nbsp;Laiba Nadeem ,&nbsp;Guendouzi Abdelkrim ,&nbsp;Aisha Khalid ,&nbsp;Khursheed Muzammil","doi":"10.1016/j.jmgm.2025.109220","DOIUrl":"10.1016/j.jmgm.2025.109220","url":null,"abstract":"<div><div><em>Mycobacterium tuberculosis</em> causes tuberculosis (TB), which remains a significant health problem worldwide. The rise of multidrug-resistant bacteria has worsened the situation, and current treatments are becoming less effective. InhA, a key enzyme involved in mycolic acid biosynthesis, is a validated therapeutic target in anti-TB therapy. This study aimed to explore the chemical diversity of natural substances from mushrooms against TB. Experimentally validated inhibitors from ChEMBL were retrieved to generate machine learning–based QSAR models combining nine chemical fingerprints and rigorous feature selection. The optimal RF–SVM-RFE model displayed high prediction performance (accuracy = 0.953, ROC_AUC = 0.971) and led virtual screening of mushroom metabolites. Six top-ranked compounds, including Inoscavin A and Schizine A, displayed substantial binding affinities (−11.7 to −10.5 kcal/mol) and stable interaction networks in molecular docking and MD simulations. Explainable AI (SHAP and LIME) showed fundamental structural motifs that drive activity and enhance chemical interpretability. These findings suggest promising natural scaffolds for anti-TB drug development and underscore the importance of AI-driven strategies in accelerating natural product–based therapeutics.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109220"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575960","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":"Atomic density distributions in proteins: structural and functional implications","authors":"Sotirios Touliopoulos,&nbsp;Nicholas M. Glykos","doi":"10.1016/j.jmgm.2025.109228","DOIUrl":"10.1016/j.jmgm.2025.109228","url":null,"abstract":"<div><div>Atomic packing is an important metric for characterizing protein structures, as it significantly influences various features including the stability, the rate of evolution and the functional roles of proteins. Packing in protein structures is a measure of the overall proximity between the proteins’ atoms and it can vary notably among different structures. However, even single domain proteins do not exhibit uniform packing throughout their structure. Protein cores in the interior tend to be more tightly packed compared to the protein surface and the presence of cavities and voids can disrupt that internal tight packing too.</div><div>Many different methods have been used to measure the quality of packing in proteins, identify factors that influence it, and their possible implications. In this work, we examine atomic density distributions derived from 21,255 non-redundant protein structures and show that statistically significant differences between those distributions are present. The biomolecular assembly unit was chosen as a representative for these structures. Addition of hydrogen atoms and solvation was also performed to emulate a faithful representation of the structures <em>in vitro</em>.</div><div>Several protein structures deviate significantly and systematically from the average packing behavior. Hierarchical clustering indicated that there are groups of structures with similar atomic density distributions. Search for common features and patterns in these clusters showed that some of them include proteins with characteristic structures such as coiled-coils and cytochromes. Certain classification families such as hydrolases and transferases have also a preference to appear more frequently in dense and loosely-packed clusters respectively.</div><div>Regarding factors influencing packing, our results support knowledge that larger structures have a smaller range in their density values, but tend to be more loosely packed, compared to smaller proteins. We also used indicators, like crystallographic water molecules abundance and B-factors as estimates of the stability of the structures to reveal its relationship with packing.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109228"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575961","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":"The potential of gold clusters as an innovative carrier for the nitrosoureas anticancer drug: Detailed view of NBO and QTAIM analyses","authors":"Fatemeh Shams ,&nbsp;Sharieh Hosseini ,&nbsp;Kambiz Larijani ,&nbsp;Marjan Jebeli Javan ,&nbsp;Sepideh Ketabi","doi":"10.1016/j.jmgm.2025.109174","DOIUrl":"10.1016/j.jmgm.2025.109174","url":null,"abstract":"<div><div>This study employed density functional theory (DFT) calculations to explore the interactions between nitrosourea (Nu) and small gold clusters, specifically Au4 and Au6. The nature of these interactions was further clarified using the quantum theory of atoms in molecules (QTAIM) and natural bond orbital (NBO) analyses. The results reveal that the Nu drug interacts strongly with the gold clusters. Among the conformers studied, the most stable Au4 complex exhibits a binding energy of −23.31 kcal/mol. In comparison, the most stable Au6 conformer, Nu/Au6 (1), shows binding energies of −9.54 kcal/mol in the gas phase and −9.57 kcal/mol in aqueous solution. Importantly, the electronic structure of the Aun clusters undergoes significant changes upon interaction with the Nu molecule. The decrease in the energy gap of the stable complexes serves as a chemical indicator of Nu adsorption. Furthermore, the dipole moments of the complexes increase by 3 %–36 % in aqueous environments compared to the gas phase. Analysis of the Au–O bond in the Nu complexes indicates that the bond is strongest in the Nu–Au4 (2) complex relative to the others studied. Consequently, Nu/Aun complexes show great potential as carriers for delivering the Nu molecule, owing to their strong adsorption energies and good solubility in polar solvents. These findings could contribute to future advancements in nanomedicine.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109174"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106254","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":"Modification of graphenylene nanostructure as a promising material for adsorption and sensing of 5-fluorouracil: First-principles investigations","authors":"Ali B.M. Ali ,&nbsp;A.A. Abdulazez ,&nbsp;G. Padma Priya ,&nbsp;Subhashree Ray ,&nbsp;Amrita Pal ,&nbsp;Renu Sharma ,&nbsp;Sulton Usanov ,&nbsp;Zukhra Atamuratova ,&nbsp;Elyor Saitov ,&nbsp;Aseel Smerat ,&nbsp;Mohammad Y. Alshahrani","doi":"10.1016/j.jmgm.2025.109210","DOIUrl":"10.1016/j.jmgm.2025.109210","url":null,"abstract":"<div><div>Studies aimed at developing materials with high efficacy for detecting the anticancer drug 5-fluorouracil (5FUL) are considered essential for both environmental protection and human health. However, practical utilization of such materials requires overcoming several challenges. In this study, the adhesion behavior of 5FUL on graphenylene (GrNL), a two-dimensional carbon nanostructure, as well as on Al-modified GrNL (Al@GrNL), was investigated using density functional theory (DFT) calculations. For Al@GrNL, various adhesion sites and distances from the GrNL nanosheet were thoroughly examined. The electronic and structural properties, adhesion energies, bandgap values, and adhesion energies of different complexes were analyzed and discussed in detail. The results indicate that the six-ring site (H2) is the most favorable adhesion site for the Al atom. Adhesion energy analysis reveals a strong interaction between 5FUL molecules and Al@GrNL (Ead = −1.253 eV), whereas the interaction with pure GrNL is relatively weak. Furthermore, Al@GrNL exhibits high sensitivity to 5FUL, as evidenced by significant changes in electrical conductance and magnetic properties upon adhesion. These DFT calculations provide valuable insights into the potential of Al@GrNL as an effective chemical sensor for 5FUL detection.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109210"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459013","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":"QSAR study and molecular modeling of pleuromutilin derivatives to predict antibacterial activity: Chemometrics and docking studies","authors":"Masoomeh Naseri ,&nbsp;Ali Niazi ,&nbsp;Elaheh Omidi ,&nbsp;Kowsar Bagherzadeh","doi":"10.1016/j.jmgm.2025.109208","DOIUrl":"10.1016/j.jmgm.2025.109208","url":null,"abstract":"<div><div>Quantitative structure-activity relationship (QSAR) as one of the most important fields of chemometrics provides useful information in pharmaceutical chemistry, drug design, toxicology, and other related areas. In this study, a QSAR model was developed employing image pixels of molecular structure as descriptors to describe the antibacterial activity of pleuromutilin derivatives. Accordingly, a dataset of 54 Pleuromutilin derivatives was collected from the literature, plotted, and transformed into pixels using MATLAB software to generate the desired model. The data were pre-processed through mean-centering and scaling to optimize pixel selection. The suitable descriptors were then extracted using principal component analysis (PCA) and the final model was built followed by partial least squares (PLS) multivariate calibration. The developed QSAR model was validated and novel compounds with antibacterial properties were suggested. In addition, molecular docking studies were utilized to investigate inhibitory activities of proposed new compounds against ribosomal protein (PDB ID: 3G4S). The findings showed high potential of proposed compounds to form strong interactions with key active site residues, confirming their good antibacterial activity and the validity of constructed QSAR model for drug prediction.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109208"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145444928","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":"MolContraCLIP: Structurally similar molecule retrieval algorithm based on graph neural network and CLIP model","authors":"Huiwen Long,&nbsp;Yongquan Jiang,&nbsp;Yan Yang,&nbsp;Kuanping Gong","doi":"10.1016/j.jmgm.2025.109172","DOIUrl":"10.1016/j.jmgm.2025.109172","url":null,"abstract":"<div><div>Molecular similarity assessment is pivotal in drug discovery and materials science, yet conventional methods often fail to integrate complementary 2D topological and 3D geometric information effectively. Inspired by Radford et al. (2021) the cross-modal alignment capability of Contrastive Language-Image Pretraining (CLIP), this study proposes a novel graph neural network (GNN) framework that unifies 2D and 3D molecular representations through a CLIP-inspired contrastive learning strategy. Our dual-channel architecture employs a Graph Isomorphism Network (GIN) for 2D topology encoding and a Graph Attention Network (GAT) for 3D spatial feature extraction. These modality-specific embeddings are aligned in a shared latent space via the InfoNCE loss, emulating CLIP’s paradigm to maximize mutual information between 2D and 3D molecular structures. Extensive experiments on the QM9 dataset demonstrate that our model significantly outperforms traditional fingerprint-based methods and pure GNN baselines in molecular similarity assessment. Ablation studies further validate the critical role of cross-modal contrastive learning in bridging structural information. The framework exhibits robust generalizability across diverse molecular types, offering a pioneering adaptation of CLIP’s principles to non-visual domains. This work advances multimodal representation learning in cheminformatics and opens avenues for future applications in molecular-text retrieval and drug design.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109172"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092150","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":"Structure-guided discovery of marine natural products as glucokinase activators for type 2 diabetes mellitus: A computational perspective","authors":"Heyram Krishnakumar ,&nbsp;Manikandan Jayaraman ,&nbsp;Dhamodharan Prabhu ,&nbsp;Jeyaraman Jeyakanthan","doi":"10.1016/j.jmgm.2025.109181","DOIUrl":"10.1016/j.jmgm.2025.109181","url":null,"abstract":"<div><div>Diabetes is a prevalent metabolic disorder and the ninth leading cause of mortality worldwide. Despite the availability of effective hypoglycemic agents, there remains an urgent need for more potent therapeutics with minimal adverse effects. Targeting key metabolic regulators, such as enzymes, transporters, and receptors, offers promising avenues for drug discovery. Glucokinase (GCK), a pivotal enzyme in glucose metabolism, catalyzes the conversion of glucose into glucose-6-phosphate and functions as a glucose sensor, making it a highly attractive therapeutic target for Type 2 Diabetes Mellitus (T2DM). This study investigates the potential of marine-derived bioactive compounds as GCK activators. Structure-based virtual screening (SBVS) of approximately 32,000 marine natural products (MNPs) against human GCK (PDB ID: <span><span>1V4S</span><svg><path></path></svg></span>) identified four promising candidates: CMNPD6570, CMNPD5231, SWMDBB001, and SWMDBB004. These MNPs exhibited favorable binding affinity scores (ranging from −8.80 to −12.62 kcal/mol) and formed key interactions with critical residues, including Tyr61, Arg63, Thr65, Tyr214, and Tyr215. Additionally, MM-GBSA binding free energy calculations (−89.54 to −115.66 kcal/mol) and MM-PBSA analysis (−93.05 to −306.18 kJ/mol) further supported their strong binding affinity. Pharmacokinetic and toxicity predictions indicated favorable drug-like properties for all identified MNPs. All-atom molecular dynamics (MD) simulations for 300 ns demonstrated enhanced structural stability of these compounds compared to the native ligand. Notably, CMNPD6570 and SWMDBB004 exhibited stable GCK binding, with low RMSD values and minimal fluctuations in key residues. Furthermore, free energy landscape (FEL) analysis using principal component (PC) projections confirmed the stability of these interactions. Overall, these findings underscore the potential of marine-derived bioactive compounds as novel GCK activators, laying a strong foundation for future experimental validation and the development of therapeutics for T2DM.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109181"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244580","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":"Predictive modeling of physicochemical properties of antihypertensive drugs using degree-based topological indices and machine learning algorithm","authors":"Saood Azam,&nbsp;Sadia Noureen,&nbsp;Tasra Yaqoob","doi":"10.1016/j.jmgm.2025.109189","DOIUrl":"10.1016/j.jmgm.2025.109189","url":null,"abstract":"<div><div>Quantitative prediction of physicochemical properties through molecular graph theory has become an important focus in cheminformatics. This study introduces a set of degree-based topological indices—ABC, ABS, MMR, SDD, SI, SO, SO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, and SO<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>—to model 23 antihypertensive drugs. A QSPR framework is developed using both classical linear regression and ensemble-based machine learning algorithms (Random Forest and XGBoost). Model performance is evaluated using standard error metrics (MAE, MSE, RMSE, <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), and feature importance is analyzed through Gini, permutation, and Shapley Additive exPlanations (SHAP). The proposed indices show strong correlations with boiling point, melting point, critical volume, LogP, molar refractivity, and CLogP. Among the tested models, XGBoost performs best, achieving <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>99</mn></mrow></math></span> across all properties. Beyond predictive accuracy, the findings show that degree-based indices capture structural features of drug molecules while offering interpretable insights into lipophilicity, stability, and thermodynamic behavior. These results demonstrate the potential of graph-theoretical descriptors as cost-effective alternatives to experimental assays, thereby accelerating rational drug design and screening workflows. Overall, this study establishes a generalizable modeling framework that bridges mathematical chemistry and pharmaceutical applications, providing valuable directions for high-throughput drug discovery.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109189"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267855","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":"Reactivity, bioactivity, and antileishmanial activity of dihydrosyrindine and syringine: Modelling, cytotoxicity, molecular docking, molecular dynamics, and MM-GBSA analyses","authors":"Oussama Khaoua","doi":"10.1016/j.jmgm.2025.109183","DOIUrl":"10.1016/j.jmgm.2025.109183","url":null,"abstract":"<div><div>Dihydrosyrindine (<strong>a</strong>) and Syringine (<strong>b</strong>) are phenylpropanoid derivatives with structural variations that may influence their biological activity, particularly against <em>Leishmania major</em>. This study investigates the impact of chirality on their bioactivity by assessing molecular, electronic, pharmacokinetic, and cytotoxic properties through computational methods to evaluate their potential as therapeutic agents.</div><div>Electronic analyses (HOMO–LUMO, MESP, NCI-RDG) revealed that dihydrosyrindine possesses greater electronic delocalization and a lower HOMO-LUMO gap than syringine, suggesting higher reactivity. Molecular docking against <em>L. major</em> methionyl-tRNA synthetase (PDB: 3KFL) showed stronger binding for dihydrosyrindine (−138.905 MolDock score) compared to syringine (−135.958), though both were weaker than the co-crystallized ligand ME8 (−196.543). Molecular dynamics confirmed the stability of the complexes, with dihydrosyrindine showing lower RMSD values (about 1.6 Å), indicating stronger binding retention of syringine. Syringine demonstrated strong and stable binding energies throughout the simulation (−66.81 to −76.04 kcal/mol), outperforming ME8 at later frames, whose binding energy decreased from −106.95 to −65.48 kcal/mol. In contrast, dihydrosyrindine showed weaker and unstable binding, with values fluctuating and dropping as low as −6.62 kcal/mol, indicating lower affinity and complex stability compared to both Syringine and ME8. Pharmacokinetic predictions revealed moderate intestinal absorption (about 40 %) and low CNS penetration. Both compounds lacked CYP or hERG liabilities; syringine showed better predicted clearance, while dihydrosyrindine exhibited higher environmental toxicity. Biological outcome predictions showed moderate cytotoxicity for both compounds against HL-60 (<em>leukemia</em>) and NCI-H838 (<em>lung cancer</em>) cell lines. However, both also exhibited non-selective effects on <em>normal lung fibroblasts</em> (WI-38 VA13), suggesting limited therapeutic windows.</div><div>Dihydrosyrindine demonstrates stronger reactivity and enzyme binding, indicating greater antiprotozoal potential, whereas syringine shows improved metabolic stability and consistent target engagement. The increased chirality in dihydrosyrindine enhances molecular recognition, leading to improved hydrogen bonding and hydrophobic interactions compared to syringine. However, ME8 remains the strongest binder due to its optimized interaction profile, supporting their potential as lead structures for anti-<em>Leishmania</em> drug development.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109183"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267856","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":"Density functional modelling of lead-free Sn-based AmSnX3 (Am=Rb, Cs; X=Cl, Br, I) perovskites as sustainable materials for optoelectronics and solar cell applications","authors":"Zia ur Rehman ,&nbsp;Ayesha Saddiqa ,&nbsp;A.F. Abd El-Rehim ,&nbsp;Amna Parveen ,&nbsp;Heba Y. Zahran ,&nbsp;Zeesham Abbas","doi":"10.1016/j.jmgm.2025.109227","DOIUrl":"10.1016/j.jmgm.2025.109227","url":null,"abstract":"<div><div>This research uses density functional theory to examine the structural, electronic, optical, mechanical, thermodynamic, thermoelectric, magnetic, and photovoltaic properties of Sn-based AmSnX₃ (Am = Rb, Cs; X = Cl, Br, I) perovskites. The tolerance factor (0.71–1.04) and the negative cohesive energy confirm the materials’ structural and thermodynamic stability. The electronic properties show semiconducting behavior, with energy band gaps of 0.42 eV for RbSnBr3 and 1.02 eV for CsSnCl₃. The maximum absorption values (7.16 × 10⁴ cm⁻¹ to 8.11 × 10⁴ cm⁻¹) in the visible region indicate efficient light harvesting for solar cell applications. Mechanical properties suggest mechanical stability and ductility. The Debye temperature (146.08–190.78 K) offers insights into heat capacity and thermal behavior at different temperatures. The Seebeck coefficient at room temperature classifies RbSnBr₃, RbSnI₃, and CsSnCl₃ as p-type materials, while RbSnCl₃, CsSnBr₃, and CsSnI₃ are n-type. The calculated power conversion efficiencies, from 12.47 % for CsSnI₃ to 29.54 % for CsSnCl₃, emphasize the novelty of this work, which combines structural, mechanical, electronic, and optical analyses for the first time to thoroughly evaluate the potential of RbSnX₃ and CsSnX₃ perovskites in optoelectronic and energy applications.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109227"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145573802","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}