Pub Date : 2025-10-10DOI: 10.1016/j.jmgm.2025.109190
Dandan Li , Wenpeng Wang , Xinwei Cao
The thermal decomposition mechanisms of 5-(dinitromethylene)-4,5-dihydro-1H-1,2,4-triazole (DNMDHT), a FOX-7 derivative, were systematically investigated under extreme conditions via ReaxFF-lg reactive molecular dynamics simulations. Two distinct regimes were examined: (1) high-temperature conditions (2500–3500 K) and (2) combined high-temperature-high-pressure conditions (3000 K, 0–50 GPa). There are two possible decomposition pathways for DNMDHT-FOX, one of which is that the DNMDHT-FOX molecule will first undergo condensation under high-temperature, and polymerized to form a polymer under high-pressure, then the decomposition pathway initiates with sequential C-N bond cleavages, first eliminating nitro groups followed by ring-opening, succeeded by C=C and C=N bond ruptures. Primary decomposition products include H2O, CO2, N2, H2, and NH3 as stable products, with NO2, NO, and CO identified as key intermediates. Notably, pressure-dependent studies revealed NH3 yields increase monotonically with pressure (0–50 GPa), while all other product yields demonstrate inverse pressure dependence. These findings establish that temperature accelerates decomposition kinetics whereas pressure exerts an inhibitory effect, except for NH3 formation. This work provides fundamental insights into the decomposition chemistry of energetic FOX-7 derivatives under extreme conditions, offering valuable guidance for the design and safety evaluation of novel high-energy materials.
{"title":"Decomposition of 5-(Dinitromethylene)-4,5-dihydro-1H-1,2,4-triazole at elevated temperatures coupled with high pressures: A molecular dynamics study","authors":"Dandan Li , Wenpeng Wang , Xinwei Cao","doi":"10.1016/j.jmgm.2025.109190","DOIUrl":"10.1016/j.jmgm.2025.109190","url":null,"abstract":"<div><div>The thermal decomposition mechanisms of 5-(dinitromethylene)-4,5-dihydro-1H-1,2,4-triazole (DNMDHT), a FOX-7 derivative, were systematically investigated under extreme conditions via ReaxFF-lg reactive molecular dynamics simulations. Two distinct regimes were examined: (1) high-temperature conditions (2500–3500 K) and (2) combined high-temperature-high-pressure conditions (3000 K, 0–50 GPa). There are two possible decomposition pathways for DNMDHT-FOX, one of which is that the DNMDHT-FOX molecule will first undergo condensation under high-temperature, and polymerized to form a polymer under high-pressure, then the decomposition pathway initiates with sequential C-N bond cleavages, first eliminating nitro groups followed by ring-opening, succeeded by C=C and C=N bond ruptures. Primary decomposition products include H<sub>2</sub>O, CO<sub>2</sub>, N<sub>2</sub>, H<sub>2</sub>, and NH<sub>3</sub> as stable products, with NO<sub>2</sub>, NO, and CO identified as key intermediates. Notably, pressure-dependent studies revealed NH<sub>3</sub> yields increase monotonically with pressure (0–50 GPa), while all other product yields demonstrate inverse pressure dependence. These findings establish that temperature accelerates decomposition kinetics whereas pressure exerts an inhibitory effect, except for NH<sub>3</sub> formation. This work provides fundamental insights into the decomposition chemistry of energetic FOX-7 derivatives under extreme conditions, offering valuable guidance for the design and safety evaluation of novel high-energy materials.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109190"},"PeriodicalIF":3.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286405","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}
Pub Date : 2025-10-08DOI: 10.1016/j.jmgm.2025.109189
Saood Azam, Sadia Noureen, Tasra Yaqoob
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, and SO—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, ), 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 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.
{"title":"Predictive modeling of physicochemical properties of antihypertensive drugs using degree-based topological indices and machine learning algorithm","authors":"Saood Azam, Sadia Noureen, 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>></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":"2025-10-08","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}
Pub Date : 2025-10-08DOI: 10.1016/j.jmgm.2025.109187
Francesco Ferri , Marco Cannariato , Lorenzo Pallante , Eric A. Zizzi , Marcello Miceli , Marco A. Deriu
This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic taste modalities that can be perceived by humans and other mammals. It is mediated by a family of G protein-coupled receptors (GPCRs), namely taste receptor type 2 (TAS2R) or bitter taste receptors. Furthermore, TAS2Rs participate in numerous functions beyond the gustatory system and have implications for various diseases due to their expression in various extra-oral tissues. For this reason, predicting the specific ligand-TAS2Rs interactions can be useful not only in the field of taste perception but also in the broader context of drug design. Considering that in-vitro screening of potential TAS2R ligands is expensive and time-consuming, machine learning (ML) and deep learning (DL) emerged as powerful tools to assist in the selection of ligands and targets for experimental studies and enhance our understanding of bitter receptor roles. In this context, ML and DL models developed in this work are both characterized by high performance and easy applicability. Furthermore, they can be synergistically integrated to enhance model explainability and facilitate the interpretation of results. Hence, the presented models promote a comprehensive understanding of the molecular characteristics of bitter compounds and the design of novel bitterants tailored to target specific TAS2Rs of interest.
{"title":"Explainable machine learning and deep learning models for predicting TAS2R-bitter molecule interactions","authors":"Francesco Ferri , Marco Cannariato , Lorenzo Pallante , Eric A. Zizzi , Marcello Miceli , Marco A. Deriu","doi":"10.1016/j.jmgm.2025.109187","DOIUrl":"10.1016/j.jmgm.2025.109187","url":null,"abstract":"<div><div>This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic taste modalities that can be perceived by humans and other mammals. It is mediated by a family of G protein-coupled receptors (GPCRs), namely taste receptor type 2 (TAS2R) or bitter taste receptors. Furthermore, TAS2Rs participate in numerous functions beyond the gustatory system and have implications for various diseases due to their expression in various extra-oral tissues. For this reason, predicting the specific ligand-TAS2Rs interactions can be useful not only in the field of taste perception but also in the broader context of drug design. Considering that in-vitro screening of potential TAS2R ligands is expensive and time-consuming, machine learning (ML) and deep learning (DL) emerged as powerful tools to assist in the selection of ligands and targets for experimental studies and enhance our understanding of bitter receptor roles. In this context, ML and DL models developed in this work are both characterized by high performance and easy applicability. Furthermore, they can be synergistically integrated to enhance model explainability and facilitate the interpretation of results. Hence, the presented models promote a comprehensive understanding of the molecular characteristics of bitter compounds and the design of novel bitterants tailored to target specific TAS2Rs of interest.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109187"},"PeriodicalIF":3.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145301565","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}
Pub Date : 2025-10-04DOI: 10.1016/j.jmgm.2025.109188
Remmer L. Salas , Portia Mahal G. Sabido , Ricky B. Nellas
Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics, whose effectiveness is declining due to rising antimicrobial resistance (AMR). To accelerate AMP discovery, we developed ISCAPE (Interpretable Support Vector Classifier of Antibacterial Activity of Peptides against Escherichia coli), a machine learning (ML) model that addresses the limitations of current AMP predictors. ISCAPE requires only a Simplified Molecular-Input Line-Entry System (SMILES) string as input and can predict the activity of both natural and chemically modified peptides against E. coli ATCC 25922. Activity is defined by a minimum inhibitory concentration (MIC) threshold of ≤16 μg/mL. To ensure reliability, only MIC values obtained under comparable experimental conditions were included in our curated dataset. ISCAPE outperformed the state-of-the-art AntiMPmod, achieving an area under the receiver operating characteristic curve (AUROC) of 91.83% and a Matthew's correlation coefficient (MCC) of 71.86%. Features driving this performance include the fraction of carbon-carbon pairs and feature- and count-based extended connectivity fingerprints (ECFPs). Model interpretability is enhanced through SHapley Additive exPlanations (SHAP), which identifies the molecular features most critical for AMP activity. To our knowledge, ISCAPE is the first interpretable ML predictor capable of predicting antibacterial activity for both natural and modified peptides against a specific E. coli strain. It is a user-friendly tool that allows experimentalists to pinpoint key molecular features, reducing the need for extensive structure-activity relationship (SAR) studies and guiding the design of novel AMPs.
{"title":"Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli","authors":"Remmer L. Salas , Portia Mahal G. Sabido , Ricky B. Nellas","doi":"10.1016/j.jmgm.2025.109188","DOIUrl":"10.1016/j.jmgm.2025.109188","url":null,"abstract":"<div><div>Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics, whose effectiveness is declining due to rising antimicrobial resistance (AMR). To accelerate AMP discovery, we developed ISCAPE (<strong>I</strong>nterpretable <strong>S</strong>upport Vector <strong>C</strong>lassifier of <strong>A</strong>ntibacterial Activity of <strong>P</strong>eptides against <strong><em>E</em></strong><em>scherichia coli</em>), a machine learning (ML) model that addresses the limitations of current AMP predictors. ISCAPE requires only a Simplified Molecular-Input Line-Entry System (SMILES) string as input and can predict the activity of both natural and chemically modified peptides against <em>E. coli</em> ATCC 25922. Activity is defined by a minimum inhibitory concentration (MIC) threshold of ≤16 μg/mL. To ensure reliability, only MIC values obtained under comparable experimental conditions were included in our curated dataset. ISCAPE outperformed the state-of-the-art AntiMPmod, achieving an area under the receiver operating characteristic curve (AUROC) of 91.83% and a Matthew's correlation coefficient (MCC) of 71.86%. Features driving this performance include the fraction of carbon-carbon pairs and feature- and count-based extended connectivity fingerprints (ECFPs). Model interpretability is enhanced through SHapley Additive exPlanations (SHAP), which identifies the molecular features most critical for AMP activity. To our knowledge, ISCAPE is the first interpretable ML predictor capable of predicting antibacterial activity for both natural and modified peptides against a specific <em>E. coli</em> strain. It is a user-friendly tool that allows experimentalists to pinpoint key molecular features, reducing the need for extensive structure-activity relationship (SAR) studies and guiding the design of novel AMPs.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109188"},"PeriodicalIF":3.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267854","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}
Anthrax, caused by Bacillus anthracis, remains a critical zoonotic threat, with treatment efficacy increasingly compromised by advanced infection progression and rising antibiotic resistance. This study leverages integrative computational strategies to identify and characterize novel therapeutic targets among previously uncharacterized molecular chaperones-Trigger Factor (BASTig) and peptidyl-prolyl cis-trans isomerase B (BASPpiB)-from B. anthracis Sterne. Structural elucidation using homology modelling and AlphaFold revealed distinctive architectures for BASTig (425 residues) and BASPpiB (145 residues). High-throughput virtual screening of diverse chemical libraries pinpointed compounds 51002 and 50423 as promising inhibitors, with strong binding affinities of −52.58 and −66.4 kcal/mol, respectively. ADME profiling confirmed favourable drug-like properties, and molecular dynamics simulations demonstrated stable protein–ligand interactions. Quantum mechanical calculations further supported the electronic complementarity and thermodynamic stability of these complexes. Electrostatic surface potential (ESP) analysis revealed that compound 51002 features predominantly positive charge distributions, favouring interactions with acidic residues in BASTig, while compound 50423 displays heterogeneous electrostatic regions, enabling adaptive binding to BASPpiB's dynamic pocket. Toxicity predictions indicated acceptable safety profiles for both leads. Immunogenicity assessment showed differential antigenic potential (BASPpiB: 100 %, BASTig: 66 %). Epitope mapping with ABCpred identified multiple high-scoring, spatially distributed B-cell epitopes in both proteins, with substantial concordance between predictive algorithms. These results highlight the therapeutic promise of targeting molecular chaperones in B. anthracis and provide a foundation for both small-molecule drug discovery and rational immunogen design, addressing urgent needs in anthrax intervention and antimicrobial resistance.
{"title":"Structure-based identification of small molecule inhibitors targeting trigger factor and peptidyl prolyl cis/trans isomerase B (PpiB) of Bacillus anthracis Sterne: Towards new therapeutic interventions against anthrax","authors":"Roopshali Rakshit , Aayush Bahl , Gargi Gautam , Saurabh Pandey , Deeksha Tripathi","doi":"10.1016/j.jmgm.2025.109185","DOIUrl":"10.1016/j.jmgm.2025.109185","url":null,"abstract":"<div><div>Anthrax, caused by <em>Bacillus anthracis</em>, remains a critical zoonotic threat, with treatment efficacy increasingly compromised by advanced infection progression and rising antibiotic resistance. This study leverages integrative computational strategies to identify and characterize novel therapeutic targets among previously uncharacterized molecular chaperones-Trigger Factor (BASTig) and peptidyl-prolyl <em>cis-trans</em> isomerase B (BASPpiB)-from <em>B. anthracis</em> Sterne. Structural elucidation using homology modelling and AlphaFold revealed distinctive architectures for BASTig (425 residues) and BASPpiB (145 residues). High-throughput virtual screening of diverse chemical libraries pinpointed compounds 51002 and 50423 as promising inhibitors, with strong binding affinities of −52.58 and −66.4 kcal/mol, respectively. ADME profiling confirmed favourable drug-like properties, and molecular dynamics simulations demonstrated stable protein–ligand interactions. Quantum mechanical calculations further supported the electronic complementarity and thermodynamic stability of these complexes. Electrostatic surface potential (ESP) analysis revealed that compound 51002 features predominantly positive charge distributions, favouring interactions with acidic residues in BASTig, while compound 50423 displays heterogeneous electrostatic regions, enabling adaptive binding to BASPpiB's dynamic pocket. Toxicity predictions indicated acceptable safety profiles for both leads. Immunogenicity assessment showed differential antigenic potential (BASPpiB: 100 %, BASTig: 66 %). Epitope mapping with ABCpred identified multiple high-scoring, spatially distributed B-cell epitopes in both proteins, with substantial concordance between predictive algorithms. These results highlight the therapeutic promise of targeting molecular chaperones in <em>B. anthracis</em> and provide a foundation for both small-molecule drug discovery and rational immunogen design, addressing urgent needs in anthrax intervention and antimicrobial resistance.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109185"},"PeriodicalIF":3.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232652","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}
Pub Date : 2025-10-03DOI: 10.1016/j.jmgm.2025.109186
Beow Keat Yap , Scott Palmer , Thomas Piccariello
Previous studies reported that the acid congener of the cannabinoids, cannabidiolic acid, was approximately 1000 times more effective than the neutral congener, cannabidiol, in alleviating emesis. The biological actions of cannabinoids were proposed to be mediated by the enhancement of somatodendritic 5-HT1A receptors. However, to date, the potential mechanism that may be involved in the enhancement of the 5-HT1A activity by the acid congener is still lacking. To address this gap, molecular docking and molecular dynamics simulations were performed on different pairs of neutral and acidic cannabinoids in a human 5-HT1A receptor model. Analyses showed that simulated cannabinoid acids (cannabidiolic acid and tetrahydrocannabivarinic acid) and tetrahydrocannabivarin were preferentially bound at the allosteric site of 5-HT1A and were able to maintain the receptor in its active state when a full agonist, R(+)-8-OH-DPAT, was bound at the orthosteric site. Importantly, these results also suggest that the strong activity of cannabidiolic acid is not due to its strong affinity for the 5-HT1A receptor but its positive allosteric modulation of the agonist activity on 5-HT1A, presumably by blocking the exit of the orthosteric ligand, hence promoting continuous activation of the receptor. This study also demonstrates that cannabidiol and both neutral and acidic cannabigerol prefer binding at the orthosteric site and are potential partial agonists of 5-HT1A. In conclusion, these findings propose that every cannabinoid, regardless of whether neutral or acidic, is unique on its own in terms of its binding and function.
{"title":"In silico insights on the binding site and function of cannabinoids and cannabinoid acids on human 5-HT1A receptor","authors":"Beow Keat Yap , Scott Palmer , Thomas Piccariello","doi":"10.1016/j.jmgm.2025.109186","DOIUrl":"10.1016/j.jmgm.2025.109186","url":null,"abstract":"<div><div>Previous studies reported that the acid congener of the cannabinoids, cannabidiolic acid, was approximately 1000 times more effective than the neutral congener, cannabidiol, in alleviating emesis. The biological actions of cannabinoids were proposed to be mediated by the enhancement of somatodendritic 5-HT<sub>1A</sub> receptors. However, to date, the potential mechanism that may be involved in the enhancement of the 5-HT<sub>1A</sub> activity by the acid congener is still lacking. To address this gap, molecular docking and molecular dynamics simulations were performed on different pairs of neutral and acidic cannabinoids in a human 5-HT<sub>1A</sub> receptor model. Analyses showed that simulated cannabinoid acids (cannabidiolic acid and tetrahydrocannabivarinic acid) and tetrahydrocannabivarin were preferentially bound at the allosteric site of 5-HT<sub>1A</sub> and were able to maintain the receptor in its active state when a full agonist, R(+)-8-OH-DPAT, was bound at the orthosteric site. Importantly, these results also suggest that the strong activity of cannabidiolic acid is not due to its strong affinity for the 5-HT<sub>1A</sub> receptor but its positive allosteric modulation of the agonist activity on 5-HT<sub>1A</sub>, presumably by blocking the exit of the orthosteric ligand, hence promoting continuous activation of the receptor. This study also demonstrates that cannabidiol and both neutral and acidic cannabigerol prefer binding at the orthosteric site and are potential partial agonists of 5-HT<sub>1A</sub>. In conclusion, these findings propose that every cannabinoid, regardless of whether neutral or acidic, is unique on its own in terms of its binding and function.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109186"},"PeriodicalIF":3.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244577","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}
This work presents a computational analysis of a series of azulene-based push-pull chromophores (A1–A10) with customized nonlinear optical (NLO) characteristics, targeting advanced applications in photonics and optoelectronics. By employing density functional theory (DFT) and time dependent-DFT (TD-DFT), we systematically assessed the influence of solvent polarity on first, second, and third order polarizabilities, natural transition orbitals, and UV–Visible absorption spectra. The key results indicate that strategic acceptor substitutions and extended conjugation length lead to enhanced multi-order nonlinear optical responses, with the derivative A8 showing remarkable octupolar contribution. The reduction in the energy gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) has promoted effective intramolecular charge transfer, especially in derivatives A6, A7, A9, and A10, which displayed all-order NLO characteristics. In contrast, A2 and A4 were characterized by predominant second-order responses, while A8 exhibited both first and third order responses. By correlating solvent environments with nonlinear optical performance, this computational study demonstrates dynamic tunability of these materials, which paves the way for their applications in optical limiters, photomultipliers and photorefractive devices. The findings of this study highlight the promise of azulene derivatives as flexible building blocks for the next generation of photonic and optoelectronic technologies.
{"title":"Solvent-dependent electronic, photophysical and nonlinear optical properties of azulene-based push-pull chromophores: A DFT approach","authors":"Dhanya P.K. , Arjun J. , Navjot Kaur , Renjith Raveendran Pillai","doi":"10.1016/j.jmgm.2025.109180","DOIUrl":"10.1016/j.jmgm.2025.109180","url":null,"abstract":"<div><div>This work presents a computational analysis of a series of azulene-based push-pull chromophores (A1–A10) with customized nonlinear optical (NLO) characteristics, targeting advanced applications in photonics and optoelectronics. By employing density functional theory (DFT) and time dependent-DFT (TD-DFT), we systematically assessed the influence of solvent polarity on first, second, and third order polarizabilities, natural transition orbitals, and UV–Visible absorption spectra. The key results indicate that strategic acceptor substitutions and extended conjugation length lead to enhanced multi-order nonlinear optical responses, with the derivative A8 showing remarkable octupolar contribution. The reduction in the energy gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) has promoted effective intramolecular charge transfer, especially in derivatives A6, A7, A9, and A10, which displayed all-order NLO characteristics. In contrast, A2 and A4 were characterized by predominant second-order responses, while A8 exhibited both first and third order responses. By correlating solvent environments with nonlinear optical performance, this computational study demonstrates dynamic tunability of these materials, which paves the way for their applications in optical limiters, photomultipliers and photorefractive devices. The findings of this study highlight the promise of azulene derivatives as flexible building blocks for the next generation of photonic and optoelectronic technologies.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109180"},"PeriodicalIF":3.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251550","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}
Pub Date : 2025-10-01DOI: 10.1016/j.jmgm.2025.109183
Oussama Khaoua
Dihydrosyrindine (a) and Syringine (b) are phenylpropanoid derivatives with structural variations that may influence their biological activity, particularly against Leishmania major. 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.
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 L. major 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 (leukemia) and NCI-H838 (lung cancer) cell lines. However, both also exhibited non-selective effects on normal lung fibroblasts (WI-38 VA13), suggesting limited therapeutic windows.
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-Leishmania drug development.
{"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":"2025-10-01","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}
Pub Date : 2025-09-30DOI: 10.1016/j.jmgm.2025.109184
Nihat Karakuş, Dilara Özbakır Işın
Next-generation OLEDs require more than just incremental improvements; they necessitate a fundamental rethinking of how excited-state dynamics are controlled. Central to this challenge is the singlet–triplet energy gap (ΔEST), which plays a crucial role in determining whether the abundant triplet excitons are dissipated as heat or harvested as light. When ΔEST decreases below 0.2 eV, reverse intersystem crossing (RISC) occurs with remarkable efficiency. This process unlocks the full potential of thermally activated delayed fluorescence (TADF) without relying on scarce heavy metals. In this context, position isomers of BN-perylenes represent a significant breakthrough. By embedding isoelectronic B–N units at different sites of the perylene scaffold, we can reshape the orbital topology, enhance molecular polarity, and spatially confine excitons. The variation in the position of BN substitution directly tunes ΔEST, allowing for precise control over excited-state energetics and emission behavior. As a result, these isomers produce a new generation of emitters that combine high internal quantum efficiency with long-term stability and color purity. Such molecular innovations transform ΔEST from a passive limitation into an active design variable, marking a significant step toward OLED devices that are brighter, more efficient, and sustainable at scale.
{"title":"Optimizing OLED efficiency through thermally activated delayed fluorescence: Computational insights into position isomers of BN-perylenes","authors":"Nihat Karakuş, Dilara Özbakır Işın","doi":"10.1016/j.jmgm.2025.109184","DOIUrl":"10.1016/j.jmgm.2025.109184","url":null,"abstract":"<div><div>Next-generation OLEDs require more than just incremental improvements; they necessitate a fundamental rethinking of how excited-state dynamics are controlled. Central to this challenge is the singlet–triplet energy gap (ΔE<sub>ST</sub>), which plays a crucial role in determining whether the abundant triplet excitons are dissipated as heat or harvested as light. When ΔE<sub>ST</sub> decreases below 0.2 eV, reverse intersystem crossing (RISC) occurs with remarkable efficiency. This process unlocks the full potential of thermally activated delayed fluorescence (TADF) without relying on scarce heavy metals. In this context, position isomers of BN-perylenes represent a significant breakthrough. By embedding isoelectronic B–N units at different sites of the perylene scaffold, we can reshape the orbital topology, enhance molecular polarity, and spatially confine excitons. The variation in the position of BN substitution directly tunes ΔE<sub>ST</sub>, allowing for precise control over excited-state energetics and emission behavior. As a result, these isomers produce a new generation of emitters that combine high internal quantum efficiency with long-term stability and color purity. Such molecular innovations transform ΔE<sub>ST</sub> from a passive limitation into an active design variable, marking a significant step toward OLED devices that are brighter, more efficient, and sustainable at scale.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109184"},"PeriodicalIF":3.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145308317","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}
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: 1V4S) 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.
{"title":"Structure-guided discovery of marine natural products as glucokinase activators for type 2 diabetes mellitus: A computational perspective","authors":"Heyram Krishnakumar , Manikandan Jayaraman , Dhamodharan Prabhu , 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":"2025-09-29","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}
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