Journal of molecular graphics & modelling最新文献

{"title":"DeepHybridCPI: A hybrid deep learning framework for compound–protein interaction prediction","authors":"Areen Rasool,&nbsp;Jamshaid Ul Rahman,&nbsp;Qasim Ali","doi":"10.1016/j.jmgm.2026.109303","DOIUrl":"10.1016/j.jmgm.2026.109303","url":null,"abstract":"<div><div>In bioinformatics, deep learning-based methods for Compound-Protein Interaction (CPI) prediction play a vital role in virtual screening, drug discovery, and drug repositioning. Recent improvements in computational methods have shown great possibility to save costs of experiment and speed up target identification. Nevertheless, the current CPI forecasting methods remain severely limited. Many rely on shallow Graph Neural Networks (GNNs) that struggle to capture the global structural context of compounds, while conventional Convolutional Neural Networks (CNNs) focus primarily on local sequence motifs and fail to model long-range dependencies in proteins. Even though a number of recent architectures strive to solve these problems by adding complexity to models, or by adding complex modules, these additions often cause significant computational overhead. To overcome these challenges, we propose DeepHybridCPI, a hybrid deep learning framework designed for accurate and efficient CPI prediction. Our hybrid model integrates a multiscale, densely connected GNN to extract compound features capturing both local substructures and global molecular topology, and employs CNNs with Long Short-Term Memory (LSTM) networks to model both local motifs and extended dependencies in protein sequences. The learned compound and protein representations are fused into a unified latent space to enable effective interaction modeling. Experimental evaluations on benchmark Human and <em>C. elegans</em> datasets demonstrate that DeepHybridCPI consistently outperforms existing state-of-the-art baseline methods in terms of AUC, Precision, and Recall. These findings highlight the importance of combining multiscale compound representations with hybrid sequence encoders within a single unified framework, providing a promising avenue for accelerating computational drug discovery. We release our source code and dataset at: <span><span>https://github.com/jamshaidwarraich/DeepHybridCPI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109303"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146018798","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":"Investigating the anti-Alzheimer potential of biogenic compounds from Zinc15 database as NMDA antagonist: An in-silico approach","authors":"Somdatta Chaudhari ,&nbsp;Asavari Shinde ,&nbsp;Mukund Salunke ,&nbsp;Shriram Bairagi ,&nbsp;Azad Dhage ,&nbsp;Pinkal Patel ,&nbsp;Vivek Rathod ,&nbsp;Sandeep Pathare ,&nbsp;Nojood Altwaijry ,&nbsp;Mohd Shahnawaz Khan","doi":"10.1016/j.jmgm.2026.109277","DOIUrl":"10.1016/j.jmgm.2026.109277","url":null,"abstract":"<div><div>Alzheimer's disease is an unavoidable neurological disorder in which the death of brain cells brings on memory loss, cognitive decline, and eventual dementia. There is no recognized treatment for Alzheimer's illness. By the year 2050, it is expected that the global population will witness approximately 100 million cases of Alzheimer's disease (AD). Despite recognizing AD as a formidable illness for over a century, no effective cure has been discovered thus far. Synaptic dysfunction could result from disturbed synaptic calcium handling caused by excessive activation of glutamate receptors, particularly the N-methyl-D-aspartate receptors (NMDARs). Glutamate serves as the brain's primary excitatory neurotransmitter, acting on ionotropic and metabotropic glutamate receptors. In recent years, several pharmacologically active substances derived from plants, animals, and microbes have shown promise in treating AD by focusing on various pathogenic processes. Initially, we used virtual screening to assess natural product-like compounds against NMDA receptors. In this research study, we have screened a natural compound database derived from zinc15. The best candidate was then validated through molecular dynamics simulation (MDS). The results revealed that out of 4221 compounds tested, only 165 showed superior binding interactions compared to native ligands, making them inhibitors for protein. Further analysis using ADMET indicates favorable drug-like properties, particularly for CNS drug-likeness. The MDS results, including RMSD, RMSF, Rg, and residue interactions, indicated a strong and stable association between top molecules and target protein. This confirms that top molecules can effectively remain within the binding pockets of the target proteins, forming stable protein-ligand complexes.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109277"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146157389","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":"On QSPR analysis for predicting efficacy of physicochemical properties of antibiotics drugs via topological indices and regression models","authors":"Yingxuan Huang ,&nbsp;W. Eltayeb Ahmed ,&nbsp;Muhammad Farhan Hanif ,&nbsp;Saba Hanif ,&nbsp;Muhammad Imran ,&nbsp;Muhammad Kamran Siddiqui","doi":"10.1016/j.jmgm.2026.109280","DOIUrl":"10.1016/j.jmgm.2026.109280","url":null,"abstract":"<div><div>Quantitative structure property relationship(QSPR) has emerged as an indispensable tool for the estimation of physicochemical properties in drug molecules using mathematical and computational methods. Here, we introduce novel reverse degree based topological indices to see their applicability in case of selected antibiotic compounds property prediction. Reliable models to predict properties such as the boiling point, molar refractivity and enthalpy of vaporization exist to correlate molecular structure with experimentally reported physicochemical parameters. We have analyzed structurally different antibiotics with regression models developed in Python and SPSS in order to guarantee the robustness and reproducibility. We note here that predictive measures of cubic regression models seem to perform better, as observed through generally greater correlation coefficients. The results show that the reverse topological indices are efficient for recording structural differences in antibiotic molecules and they can be excellent descriptors for predicting their physical and chemical properties. It also stresses that, the use of reverse degree based descriptors on antibiotic compounds is new, providing a basis for further QSPR modeling for more general drug families. This work is part of a growing trend to study the interfaces between graph theory and cheminformatics where new indices help to improve our understanding over molecular properties with importance for drug design.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109280"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980543","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":"RLBindDeep: A ResNet-LSTM based novel framework for protein–ligand binding affinity prediction","authors":"Ekarsi Lodh ,&nbsp;Shalini Majumder ,&nbsp;Tapan Chowdhury ,&nbsp;Manashi De","doi":"10.1016/j.jmgm.2026.109282","DOIUrl":"10.1016/j.jmgm.2026.109282","url":null,"abstract":"<div><div>The prediction of the binding affinity of proteins and ligands in computational drug discovery with high accuracy is critical when evaluating the effectiveness of potential therapeutic compounds. This research work introduces RLBindDeep, a novel deep learning architecture based on the amalgamation of the ResNet and LSTM architectures, for improved accuracy in predicting protein–ligand binding affinities. Most traditional methodologies utilizing conventional molecular docking techniques suffer from poor accuracy owing to semi-flexible modeling approaches and limited considerations of complex interactions. On the other hand, RLBindDeep, which is formulated as a pose-independent binding affinity regression model that directly predicts experimental protein–ligand binding affinities from fixed complex structures, without performing docking or rescoring multiple poses, has performed well in extracting important features of the protein–ligand interaction. Specifically, the extracted features encompass ligand physicochemical descriptors (e.g., molecular weight, LogP, TPSA), protein-level features such as amino acid composition, and detailed interaction features including van der Waals, electrostatic, and hydrogen-bond energies. The model has been tested rigorously over the CASF-2016 benchmark dataset and has returned Pearson’s coefficient <span><math><mrow><mi>R</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>875</mn></mrow></math></span>, Spearman’s coefficient <span><math><mrow><mi>ρ</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>864</mn></mrow></math></span>, and Root Mean Square Error <span><math><mrow><mi>RMSE</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>993</mn></mrow></math></span>. This significantly outperforms existing state-of-the-art models, such as HAC-Net and AutoDock Vina. Improved accuracy and robustness in RLBindDeep further highlight the possibility of deep learning to revolutionize computational drug discovery processes, making strategies for drug development more efficient and targeted.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109282"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980545","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":"Repurposing drugs for EGFR-targeted cancer therapy: An in silico and in vitro study with pharmacophore-based insights","authors":"Pınar Siyah ,&nbsp;Firat Baris Barlas","doi":"10.1016/j.jmgm.2026.109288","DOIUrl":"10.1016/j.jmgm.2026.109288","url":null,"abstract":"<div><div>Cancer is the second leading cause of death globally and remains a priority due to its impact on life quality, treatment complexity, and high costs. To expedite drug development, researchers are increasingly repurposing FDA-approved drugs and clinical candidates, reducing time and costs through in silico methods. In this study, 3235 FDA-approved and clinical molecules were screened for EGFR inhibition, a significant target due to its role in cancer progression and treatment resistance. A pharmacophore model was generated based on erlotinib's co-crystallized structure and quantitative structure-activity relationships. Molecules meeting the pharmacophoric criteria underwent SP and XP docking, with thresholds of −6.00 kcal/mol and −7.00 kcal/mol, respectively, followed by anti-cancer potential analysis via MetaCore/MetaDrug and MD simulations at 1, 10, and 100 ns to assess EGFR-binding stability. For the molecule Ticagrelor, which demonstrated particularly promising results, and Erlotinib cell culture viability assays were conducted across three cell lines—cancerous A549, U87, and healthy BEAS-2B— (IC50) of, 8.2576 μM, 9.4058 μM, and 15.893 μM, respectively for Ticagrelor and 11.708 μM, 12.747 μM and 14.6709 μM, respectively for Erlotinib. In silico results highlight Ticagrelor's significant EGFR-inhibiting potential with enhanced binding stability compared to the reference.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109288"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146010770","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 investigation of complexes formed between chemical UV filters and cyclodextrins in water","authors":"Grigorios Megariotis ,&nbsp;Georgios Mikaelian ,&nbsp;Haralambos Sarimveis","doi":"10.1016/j.jmgm.2026.109322","DOIUrl":"10.1016/j.jmgm.2026.109322","url":null,"abstract":"<div><div>UV filter – cyclodextrin complexes are investigated in silico in an aqueous environment using all-atom molecular dynamics simulations in the isothermal-isobaric statistical ensemble. The UV filters considered herein are octocrylene and avobenzone, which are used in commercial sunscreen products and together provide broad-spectrum skin protection (covering UV-A to UV-B radiation). The selected host molecules are β-cyclodextrin (β-CD) and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD). In general, cyclodextrins have been assessed for protecting UV filters against photodegradation and oxidation, as well as for their ability to restrict UV filter permeation into deep skin layers. In all simulations, the starting point involves the UV filters and cyclodextrin molecules in the unbound state to determine whether noncovalent complexation is a spontaneous process. The main goal of this study is to examine in detail the complexes from a nanoscopic point of view, as well as the complexation process itself, paying particular attention to the thermodynamic description and the stability of the formed supramolecular complexes. In the framework of our analysis, several properties are calculated and, when possible, comparisons are made with published experimental data. Concerning thermodynamics, the binding free energy is estimated by applying a modified version of the Linear Interaction Energy (LIE) method. This method has been successfully applied in a number of studies involving complexes formed between cyclodextrins and small organic molecules. In the case of avobenzone, which contains a β-diketone group, both keto and enol forms are considered due to their tautomeric equilibrium and their distinct roles in photoprotection and photodegradation mechanisms.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109322"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189392","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":"Identification of molecular compounds targeting bacterial propionate metabolism with topological machine learning","authors":"Astrit Tola ,&nbsp;Shan Aziz ,&nbsp;Dannie Zhabilov ,&nbsp;Duane Winkler ,&nbsp;Mehmet Candas ,&nbsp;Baris Coskunuzer","doi":"10.1016/j.jmgm.2026.109287","DOIUrl":"10.1016/j.jmgm.2026.109287","url":null,"abstract":"<div><div>This study demonstrates the transformative potential of machine learning in drug discovery by integrating comparative protein and ligand analysis with novel topological machine learning methods. Our approach sifts through large chemical libraries to identify promising molecular structures for targeting specific proteins with high precision. While many machine learning models have proven effective on benchmark datasets, we apply these techniques to discover compounds targeting methylcitrate dehydratase (AcnD), the second enzyme in the bacterial propionate catabolism pathway. Propionate catabolism is essential in pathogenic bacteria for utilizing host derived lipids and amino acids. Inefficient removal of propionate can lead to toxic accumulation that threatens bacterial survival, making this pathway a potential antimicrobial target. We translate ligand molecular structures into topological vectors and use tailored topological models to prioritize compounds with characteristics consistent with blocking the AcnD active site. Molecular docking simulations indicate that prioritized compounds interact with key amino acid residues critical to AcnD function. Among these, 2-methylidenebutanedioic acid (itaconic acid, itaconate) ranks highly as a potential molecular scaffold for targeting AcnD. Using bacterial growth assays, we find that itaconate at 29.13 mM completely inhibits the growth of <em>Pseudomonas aeruginosa</em> and <em>Acinetobacter baumannii</em> in carbon rich liquid cultures. These findings reinforce itaconate’s potential as an antimicrobial metabolite and support the hypothesis that it can disrupt bacterial propionate catabolism, potentially by inhibiting AcnD and promoting the accumulation of toxic intermediates. Overall, our study underscores the value of integrating topology based ligand modeling with comparative sequence structure function analysis and docking to identify molecular scaffolds with favorable geometric fit, energy, and interaction profiles, guiding downstream optimization and experimental validation. Our code is available at (<span><span>https://github.com/AstritTola/Molecular-Compounds-Targeting</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109287"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980547","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":"Investigating the effect of membrane pore size on the permeability of carbon nanotubes in reverse electrodialysis using molecular dynamics simulation","authors":"Zishuo Zheng ,&nbsp;N. Emami","doi":"10.1016/j.jmgm.2026.109281","DOIUrl":"10.1016/j.jmgm.2026.109281","url":null,"abstract":"<div><div>Reverse electrodialysis (RED) is an emerging technology that converts the salinity gradient between seawater and freshwater into clean electrical energy. The efficiency of RED is strongly governed by ion transport through the membrane interface, where nanoscale geometry plays a decisive role. This study investigates, through molecular dynamics simulations, how the pore size of carbon nanotube (CNT) membranes influences water and ion permeability under an applied electric field. The simulation framework involved a two-stage procedure (30 ns equilibration at 300 K followed by dynamic evaluation of electrokinetic transport) to achieve stable atomic configurations and reliable transport data. Results revealed that as membrane pore size increased from 20 Å to 30 Å, both water flux and electric current increased significantly, indicating enhanced permeability and reduced flow resistance. However, this improvement was accompanied by a decline in hydrogen bond density from 103 to 86, implying weaker intermolecular cohesion and reduced structural confinement within larger pores. The mean-squared displacement of water molecules also increased, confirming increased molecular mobility and collision frequency. Although electric flow density decreased with pore enlargement due to charge dilution across a wider cross section, the overall voltage output rose from 21.02 to 24.54 μV. These findings demonstrate that optimizing pore geometry enables a balance between fluid transport efficiency and charge density, offering a molecular-scale understanding of how nanopore design can enhance RED performance, improve ion selectivity, and guide the development of next-generation nanoengineered membranes for sustainable energy harvesting.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109281"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079024","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":"Selectivity analysis of CDK2 inhibitors via molecular dynamics of CDK1 and CDK2","authors":"Dmitrii O. Shkil ,&nbsp;Anastasia S. Fokina ,&nbsp;Dariy T. Asainov ,&nbsp;Elena V. Petersen ,&nbsp;Andrey A. Ivashchenko ,&nbsp;Philipp Y. Maximov","doi":"10.1016/j.jmgm.2025.109266","DOIUrl":"10.1016/j.jmgm.2025.109266","url":null,"abstract":"<div><div>Cyclin-dependent kinases (CDKs) are pivotal regulators of the cell cycle and attractive therapeutic targets, particularly in breast cancer treatment. However, achieving selectivity among closely related CDKs, such as CDK2/CDK1, remains a significant challenge in drug discovery. In this study, we leverage molecular dynamics simulations and protein–ligand interaction analyses to uncover the structural and pharmacophoric determinants that drive selective inhibition of these kinases. By comparing known inhibitors and their contacts with CDK1 and CDK2, we identify critical pharmacophoric features essential for achieving target-specific selectivity. These findings provide valuable hypotheses into the design of next-generation CDK inhibitors with improved therapeutic profiles, addressing the pressing need for selective and effective cancer therapies.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109266"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959558","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":"Molecular simulations of the tunable pore structure models elucidate the adsorption of sulfamethoxazole on biochar","authors":"Zehui Zhang,&nbsp;Hong Wei,&nbsp;Feng Pan,&nbsp;Ruijie Teng,&nbsp;Junqi Song,&nbsp;Shujie Xie","doi":"10.1016/j.jmgm.2026.109276","DOIUrl":"10.1016/j.jmgm.2026.109276","url":null,"abstract":"<div><div>Biochar is an environmentally friendly adsorption material that can effectively adsorb sulfamethoxazole (SMX) in water. However, the relationship between the pore structure characteristics of biochar and the SMX adsorption process remains unclear. In this study, molecular dynamics (MD) simulations combined with complementary experiments was employed to investigate SMX adsorption on four biochar models: isolated mesopores (BC1), micropores (BC2), hierarchical pores (BC3), and amorphous carbon (BC4). A remarkable concordance was observed between the MD simulations and experimental results. MD simulations revealed that porous structures facilitate SMX adsorption, leading to the fastest adsorption equilibrium rate for BC4. Additionally, a correlation between pore size and the SMX adsorption kinetics was observed. Within micropores, SMX achieves adsorption equilibrium at a slower rate, exhibiting a diffusion coefficient 62 % lower than that observed in mesopores. Noncovalent interaction (NCI) analysis and energy decomposition demonstrated that both π-π interactions and hydrogen bonds jointly stabilize SMX adsorption on biochar, with van der Waals interaction (contributing 59 %) playing a dominant role. Experimental results showed that the adsorption of SMX onto BBC-800 conformed to the Langmuir isotherm and pseudo-second-order kinetic model. Both simulations and experiments jointly elucidated the adsorption mechanism of SMX onto biochar, primarily involving pore filling, π-π stacking, and hydrogen bonding interactions. These findings provide atomic-scale insights for designing biochar with optimized pore structures for antibiotic removal.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"144 ","pages":"Article 109276"},"PeriodicalIF":3.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928865","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}