Journal of molecular graphics & modelling最新文献

{"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}

{"title":"Computational modeling of novel perovskite oxides InXO3 (Be, Ca): Phase stable and sustainable materials for optoelectronic devices","authors":"Maaz Azeem ,&nbsp;Muhammad Khuram Shahzad ,&nbsp;Bekzod Matyakubov ,&nbsp;Ghulam Abbas Ashraf ,&nbsp;Vineet Tirth ,&nbsp;Ali Algahtani ,&nbsp;N. Sfina ,&nbsp;Naoufel Ben Hamadi","doi":"10.1016/j.jmgm.2025.109177","DOIUrl":"10.1016/j.jmgm.2025.109177","url":null,"abstract":"<div><div>Optoelectronics devices need improved performance, and there are challenges in achieving high efficiency. The perovskite compounds have become the center of attraction for their properties and characteristics. The studies on the double perovskite have gained attention due to its adjustable bandgap, better properties, and best optoelectronics applications. This study investigates the novel perovskite InXO₃ (X = Be, Ca) for structural, electronic, mechanical, phonon, and optical properties by using DFT simulations. We calculated the characteristics of perovskite using the CASTEP simulations technique and LDA-CAPZ approximation. Achieved the cubic structure, negative formation energy (InBeO₃ is −1.68 eV/atom &amp; InCaO₃ is −1.92 eV/atom), and tolerance factors (InBeO₃ is 0.84 &amp; InCaO₃ is 0.88) of both perovskite compounds predicting the stability of the materials. The electronic property disclosed that the compounds have a metallic nature. For the electronic properties, the band gap of the InBeO3 compound is 4.05 eV. After elemental substitution, the band gap of the InCaO₃ compound is reduced to 2.67 eV, which falls within the visible range and is most suitable for optoelectronics devices. The mechanical properties calculated on the positive values of coordinates C₄₄, C_11, and C₁₂ show the stability of both structures of the compounds. The excellent optical properties are calculated by full polarization for absorption, conductivity, loss function, refractive index, dielectric function &amp; loss function from the energy range that varies for both InBeO₃ and InCaO₃. The proposed results of novel perovskite InXO₃ (X = Be, Ca) oxides are a potential candidate for optoelectronics applications such as photodetectors and LEDs devices.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109177"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119051","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 molecular mechanisms underlying the function of NLuc and its variants","authors":"Mina Oliayi,&nbsp;Rahman Emamzadeh,&nbsp;Mohamad Reza Ganjalikhany","doi":"10.1016/j.jmgm.2025.109212","DOIUrl":"10.1016/j.jmgm.2025.109212","url":null,"abstract":"<div><div>Bioluminescence, the emission of light by living organisms, results from chemiluminescent reactions facilitated by enzymes like luciferases. Among these, NanoLuc (NLuc) stands out due to its exceptional brightness, stability, and compact structure, making it a valuable tool in bioassays and imaging applications. NLuc is a 19.1 kDa monomeric enzyme derived from the deep-sea shrimp <em>Oplophorus gracilirostris</em>. Its structure comprises eleven antiparallel β-strands forming a β-barrel, capped by four α-helices. To enhance its versatility, NLuc has been engineered into split forms. Despite the advancements in split NLuc applications, several impediments exist that can be addressed to improve these systems. Recently the spectacular allosteric mechanisms of NLuc has been reported. Based on that, the enzyme exhibits homotropic negative allostery, where product binding to an allosteric site inhibits substrate binding at the catalytic site. Ongoing research into its structural dynamics and allosteric behaviors continues to expand its potential applications, while efforts to enhance the efficiency of its split forms aim to broaden its utility in complex biological assays. In this <em>in silico</em> assay, we performed dynamic simulations for both the various forms of apo-NLuc and the docking complex with the substrate and product. As a result, we clarify the sources of malfunctions in split NLuc and explore various aspects of split NLuc technologies. We also examine some hypotheses of NLuc mechanisms that display the complex behavior of this luciferase.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109212"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470942","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":"Synthesis, characterization and biological evaluation of a novel chalcone derivative: Spectroscopic, DFT, docking, ADMET and MD Studies","authors":"Manikandan Anandhan ,&nbsp;Vijayaraj Venkatachalam ,&nbsp;D. Karthikeyan ,&nbsp;Sebastian Anand ,&nbsp;A.R. Prabakaran ,&nbsp;Mysoon M. Al-Ansari ,&nbsp;Sandhanasamy Devanesan","doi":"10.1016/j.jmgm.2025.109161","DOIUrl":"10.1016/j.jmgm.2025.109161","url":null,"abstract":"<div><div>E)-3-(2-hydroxy-6-methoxyphenyl)-1-(1OH-Pheno thiazin-2-yl) prop 2-en-1-one (3HM1P) was synthesized by condensing 2-acetyl phenothiazine and 2-hydroxy-3-methoxy benzaldehyde. The molecular structure of 3HM1P was confirmed by analysis and well supported by FT-IR spectrum. The compound's geometry was optimized using the B3LYP method at the 6–311++G(d,p) level, and theoretical calculations were compared to experimental data. Linear optical absorption spectra were analyzed to study solvent effects on 3HM1P, with HOMO-LUMO analysis revealing a highest energy gap of 3.27 eV in hexane. Molecular electrostatic potential (MEP) analysis identified electron-rich and electron-deficient regions, crucial for enzyme interactions. Natural bond orbital (NBO) analysis indicated a maximum stabilization energy of 392.59 kJ/mol from π→π∗ orbital overlap. Antibacterial potential was assessed through molecular docking studies against the 2JIU protein, supported by early drug-likeness evaluations and ADMET virtual screening. Molecular dynamics simulations confirmed successful docking of 3HM1P-3JUV, while normal mode analysis indicated stable and flexible molecular mobility at the binding site, with a low eigenvalue of 2.6383 × 10⁻⁰⁴.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109161"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106255","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":"Ricci-GraphDTA: A graph neural network integrating discrete Ricci curvature for drug–target affinity prediction","authors":"Xiangxiang Zheng ,&nbsp;Zhongrong Zhang ,&nbsp;Xiaona Zhang ,&nbsp;Nengzhi Jin ,&nbsp;Yunyun Zhang","doi":"10.1016/j.jmgm.2025.109170","DOIUrl":"10.1016/j.jmgm.2025.109170","url":null,"abstract":"<div><div>Drug–target affinity (DTA) prediction facilitates accelerated drug screening and reduces development costs. To enhance prediction performance and generalization capability, this paper proposes a DTA prediction model based on discrete curvature, named Ricci-GraphDTA, which integrates molecular graph and protein sequence modeling for efficient and accurate DTA prediction. The model consists of three parts: feature encoding, input representation learning, and affinity prediction. In the feature encoding stage, drug molecules are modeled as graphs, where Forman curvature is introduced to adjust the weights of neighbor information aggregation. A GIN residual network is then used to capture the local geometric and topological features of molecules. Protein sequences are modeled using BiLSTM to extract global dependency features, enhanced by an attention mechanism to capture long-range dependencies and key residue interactions—overcoming the limitations of traditional CNNs in handling long-range dependencies. In the input representation learning stage, the high-level representations of drugs and proteins are concatenated and passed through multiple nonlinear transformations to extract cross-modal interaction features, which are then used for affinity prediction. Experimental results demonstrate that Ricci-GraphDTA exhibits significant performance across various evaluation metrics on the Davis and KIBA datasets. Further cold-start experiments demonstrate the strong generalization ability of Ricci-GraphDTA in scenarios involving unseen drugs or targets, highlighting its potential in real-world drug discovery applications. On average, it achieves a 22.5% reduction in MSE across three cold-start tasks, with over 42% reduction in the dual cold-start setting, showcasing excellent structural modeling capability and robustness.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109170"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145086332","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":"Discovery of potential attention deficit and hyperactivity disorder (ADHD) drug molecules from natural compounds: In silico studies with Tanimoto similarity","authors":"Onur Ertik","doi":"10.1016/j.jmgm.2025.109200","DOIUrl":"10.1016/j.jmgm.2025.109200","url":null,"abstract":"<div><div>Attention deficit and hyperactivity disorder (ADHD) is a neurological disorder that prevents individuals from developing their potential and causes an inability to focus, especially in children, and the prevalence of ADHD in adults has been increasing. There is a very limited group of drugs for treatment of ADHD, the most important of which is methylphenidate. Therefore, the discovery of new drug molecules becomes very important because of limited drugs. In this study, methylphenidate molecule was compared with 790096 molecules by utilizing Tanimoto similarity of natural compounds and molecules were identified by second filtering according to blood-brain barrier penetration. Then, molecules were docked with dopamine transporter (DAT) and compounds with better binding scores than methylphenidate were identified and molecular dynamics simulation studies were done for the best pose and validated conformation. In addition, molecular docking and dynamic simulations were performed for monoamine oxidase A and B (MAO-A and MAO-B) in order to control the state of depression frequently encountered in ADHD individuals. The results suggest the compound 3706153 ((octahydro-1H-quinolizin-1-yl)methyl 2-(naphthalen-1-yl)acetate) has potentially inhibitory molecule for DAT, MAO-A and MAO-B and have a potential of the main structure in the development of alternative drug molecule for ADHD from the natural compounds.</div></div>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"142 ","pages":"Article 109200"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362975","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}