{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:05:04Z","timestamp":1775797504371,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Background: Cysteinyl leukotrienes are components of slow-reacting substances of anaphylactic shock (SRS-A) and play a key role in asthma and inflammatory responses. Although chromone-2-carboxylic acids and substituted (aryloxy)alkanoic acids have the potential to be SRS-A antagonists, their comprehensive structure\u2013activity relationships and pharmacokinetic characteristics remain understudied. Objective: This study integrated computational and experimental approaches, including QSAR modeling, molecular docking, ADMET analysis, molecular dynamics (MD) simulations, and antioxidant evaluation to identify and prioritize bifunctional compounds with anti-inflammatory and free radical-scavenging properties. Methods: A set of 68 compounds was analyzed using 2D and 3D quantitative structure\u2013activity relationships (QSAR) (MLR, MNLR, SVR, ANN, and atom-based partial least squares). Molecular docking and 100 ns MD simulations were performed against the CysLT1 receptor (PDB ID: 6RZ5). ADMET and drug-like properties of the compounds were predicted using ADMETlab 2.0 and SwissADME, and the in vitro antioxidant activity of the top-ranked compounds was evaluated using the DPPH method. Results: The atom-based 3D-QSAR model showed strong predictive power (R2 = 0.9524, Q2 = 0.5382). Compounds 25, 41, and 47 stood out with the most significant binding energies: \u22129.5 kcal\/mol for 25, \u221210.0 kcal\/mol for 41, and \u22129.4 kcal\/mol for 47. MD simulations confirmed the structural stability and consistent interactions of the protein-compound 47 complex. ADMET analysis showed that compounds 25 and 41 had good pharmacokinetic properties, and in vitro antioxidant assays verified their free radical-scavenging efficacy. Conclusion: Our results highlight the utility of an integrated computational\u2013experimental strategy for the discovery of dual-acting SRS-A antagonists. Compound 25 is highlighted as a promising lead compound for further preclinical development, which effectively combines leukotriene receptor antagonism and antioxidant activity. This framework provides an effective strategy for prioritizing lead compounds in anti-inflammatory drug development.<\/jats:p>","DOI":"10.3390\/ph19040600","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:26:39Z","timestamp":1775661999000},"page":"600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated QSAR, Molecular Docking, ADMET Profiling, and Antioxidant Evaluation of Substituted Chromone and Aryloxyalkanoic Acid Derivatives as Potential CysLT1 Receptor Antagonists"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5188-1130","authenticated-orcid":false,"given":"Mahboob","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Safety Engineering, Dongguk University WISE, 123 Dongdae-ro, Gyeongju-si 780714, Gyeongsangbuk-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.alit.2014.09.001","article-title":"The Role of Leukotrienes in Allergic Diseases","volume":"64","author":"Liu","year":"2015","journal-title":"Allergol. Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.jaci.2006.08.009","article-title":"The Role of Leukotrienes in Airway Inflammation","volume":"118","author":"Ogawa","year":"2006","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1093\/intimm\/dxz044","article-title":"The Leukotriene Receptors as Therapeutic Targets of Inflammatory Diseases","volume":"31","author":"Sasaki","year":"2019","journal-title":"Int. Immunol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/0006-2952(63)90077-7","article-title":"\u201cSRS-A\u201d The Slow Reacting Substance of Anaphylaxis","volume":"12","author":"Brocklehurst","year":"1963","journal-title":"Biochem. Pharmacol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1146\/annurev-pathmechdis-111523-023509","article-title":"Cysteinyl Leukotrienes in Allergic Inflammation","volume":"20","author":"Lee","year":"2025","journal-title":"Annu. Rev. Pathol. Mech. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1073\/pnas.77.6.3710","article-title":"Slow Reacting Substances of Anaphylaxis: Identification of Leukotrienes C-1 and D from Human and Rat Sources","volume":"77","author":"Lewis","year":"1980","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S220","DOI":"10.1164\/ajrccm.157.6.mar-3","article-title":"Chemistry and Structure\u2013Activity Relationships of Leukotriene Receptor Antagonists","volume":"157","author":"Bernstein","year":"1998","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1021\/jm00384a029","article-title":"Substituted (Aryloxy)Alkanoic Acids as Antagonists of Slow-Reacting Substance of Anaphylaxis","volume":"30","author":"LeMahieu","year":"1987","journal-title":"J. Med. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/S0223-5234(97)83282-5","article-title":"Synthesis and Pharmacological Evaluation of New CysLT1 Receptor Antagonists","volume":"32","author":"Griera","year":"1997","journal-title":"Eur. J. Med. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1021\/ml500298y","article-title":"Discovery of Highly Potent Dual CysLT1 and CysLT2 Antagonist","volume":"5","author":"Itadani","year":"2014","journal-title":"ACS Med. Chem. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1021\/jm00365a016","article-title":"Antagonists of Slow-Reacting Substance of Anaphylaxis. 1. Pyrido[2,1-b]Quinazolinecarboxylic Acid Derivatives","volume":"26","author":"Tilley","year":"1983","journal-title":"J. Med. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1002\/wcms.1187","article-title":"Modern 2D QSAR for Drug Discovery","volume":"4","author":"Lewis","year":"2014","journal-title":"WIREs Comput. Mol. Sci."},{"key":"ref_13","first-page":"e02202","article-title":"Combined 3D-QSAR, Molecular Docking, ADMET, and Drug-Likeness Scoring of Novel Diaminodihydrotriazines as Potential Antimalarial Agents","volume":"24","author":"Khelfa","year":"2024","journal-title":"Sci. Afr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Lian, Z., Bai, W., Chen, Y., and Xia, H. (2023). 3D- and 2D-QSAR Models\u2019 Study and Molecular Docking of Novel Nitrogen-Mustard Compounds for Osteosarcoma. Front. Mol. Biosci., 10.","DOI":"10.3389\/fmolb.2023.1164349"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"148","DOI":"10.2174\/1573409910666140410111043","article-title":"3D-QSAR Approaches in Drug Design: Perspectives to Generate Reliable CoMFA Models","volume":"10","author":"Braga","year":"2014","journal-title":"Curr. Comput. Aided-Drug Des."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pinzi, L., and Rastelli, G. (2019). Molecular Docking: Shifting Paradigms in Drug Discovery. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20184331"},{"key":"ref_17","first-page":"295","article-title":"2D-QSAR, Molecular Docking, Drug-Likeness, and ADMET\/Pharmacokinetic Predictions of Some Non-Small Cell Lung Cancer Therapeutic Agents","volume":"18","author":"Ibrahim","year":"2023","journal-title":"J. Taibah Univ. Med. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, Y., Dong, H., Peng, Y., and Welsh, W.J. (2021). QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules, 26.","DOI":"10.3390\/molecules26175270"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1007\/s11030-020-10165-4","article-title":"Classification Models and SAR Analysis on CysLT1 Receptor Antagonists Using Machine Learning Algorithms","volume":"25","author":"Wang","year":"2021","journal-title":"Mol. Divers."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8093","DOI":"10.1021\/cr3004339","article-title":"Descriptor Selection Methods in Quantitative Structure\u2013Activity Relationship Studies: A Review Study","volume":"113","author":"Shahlaei","year":"2013","journal-title":"Chem. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Grisoni, F., Ballabio, D., Todeschini, R., and Consonni, V. (2018). Molecular Descriptors for Structure\u2013Activity Applications: A Hands-On Approach. Computational Toxicology: Methods and Protocols, Springer.","DOI":"10.1007\/978-1-4939-7899-1_1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1007\/s13738-021-02426-2","article-title":"High Predictive QSAR Models for Predicting the SARS Coronavirus Main Protease Inhibition Activity of Ketone-Based Covalent Inhibitors","volume":"19","author":"Sepehri","year":"2022","journal-title":"J. Iran. Chem. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.fshw.2019.02.001","article-title":"QSAR Modeling of Benzoquinone Derivatives as 5-Lipoxygenase Inhibitors","volume":"8","author":"Rajan","year":"2019","journal-title":"Food Sci. Hum. Wellness"},{"key":"ref_24","unstructured":"Basak, S.C., Restrepo, G., and Villaveces, J.L. (2015). Advances in Mathematical Chemistry and Applications, Elsevier Inc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.aej.2024.02.055","article-title":"Comparison of Different Supervised Learning Algorithms for Position Analysis of the Slider-Crank Mechanism","volume":"92","author":"Denizhan","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"020013","DOI":"10.1063\/5.0117323","article-title":"Levenberg-Marquardt, Bayesian-Regularization, and Scaled Conjugate Gradient Algorithms for Predicting Surface Roughness Accuracy on Side Milling AISI 1045","volume":"2544","author":"Yanis","year":"2023","journal-title":"AIP Conf. Proc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1039\/C9NJ05767F","article-title":"2D and 3D-QSAR, Molecular Docking and ADMET Properties in Silico Studies of Azaaurones as Antimalarial Agents","volume":"44","author":"Hadni","year":"2020","journal-title":"New J. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"169974","DOI":"10.1016\/j.scitotenv.2024.169974","article-title":"A Machine Learning-Based QSAR Model Reveals Important Molecular Features for Understanding the Potential Inhibition Mechanism of Ionic Liquids to Acetylcholinesterase","volume":"915","author":"Wu","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X., Liang, X., Tan, M., and Huang, Z. (2020). Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front. Chem., 8.","DOI":"10.3389\/fchem.2020.00726"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.addr.2015.01.009","article-title":"The Application of in Silico Drug-Likeness Predictions in Pharmaceutical Research","volume":"86","author":"Tian","year":"2015","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Komura, H., Watanabe, R., and Mizuguchi, K. (2023). The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics, 15.","DOI":"10.3390\/pharmaceutics15112619"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.drudis.2014.01.005","article-title":"Finding the Rules for Successful Drug Optimisation","volume":"19","author":"Yusof","year":"2014","journal-title":"Drug Discov. Today"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"501","DOI":"10.3390\/encyclopedia3020035","article-title":"Molecular Filters in Medicinal Chemistry","volume":"3","author":"Kralj","year":"2023","journal-title":"Encyclopedia"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vilar, S., and Costanzi, S. (2012). Predicting the Biological Activities Through QSAR Analysis and Docking-Based Scoring. Membrane Protein Structure and Dynamics: Methods and Protocols, Springer.","DOI":"10.1007\/978-1-62703-023-6_16"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez, D., and Caballero, J. (2016). Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?. Int. J. Mol. Sci., 17.","DOI":"10.3390\/ijms17040525"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s12551-013-0130-2","article-title":"Receptor\u2013Ligand Molecular Docking","volume":"6","author":"Guedes","year":"2014","journal-title":"Biophys. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"C857","DOI":"10.1152\/ajpcell.00281.2019","article-title":"CysLT1 Receptor Antagonists Pranlukast and Zafirlukast Inhibit LRRC8-Mediated Volume Regulated Anion Channels Independently of the Receptor","volume":"317","author":"Figueroa","year":"2019","journal-title":"Am. J. Physiol.-Cell Physiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1002\/jccs.201500143","article-title":"Synthesis, Spectral Characterization and Antioxidant Activity of Novel Zafirlukast Sulfonyl Derivatives","volume":"63","author":"Rajasekhar","year":"2016","journal-title":"J. Chin. Chem. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1089\/ars.2007.1960","article-title":"Does Leukotriene Affect Intracellular Glutathione Redox State in Cultured Human Airway Epithelial Cells?","volume":"10","author":"Wang","year":"2008","journal-title":"Antioxid. Redox Signal."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9396425","DOI":"10.1155\/2017\/9396425","article-title":"Resveratrol and Montelukast Alleviate Paraquat-Induced Hepatic Injury in Mice: Modulation of Oxidative Stress, Inflammation, and Apoptosis","volume":"2017","author":"Abdeltawab","year":"2017","journal-title":"Oxid. Med. Cell. Longev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3347\/PHD.24046","article-title":"CysLT Receptor-Mediated NOX2 Activation Is Required for IL-8 Production in HMC-1 Cells Induced by Trichomonas vaginalis-Derived Secretory Products","volume":"62","author":"Lee","year":"2024","journal-title":"Parasites Hosts Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1007\/s11224-022-02008-9","article-title":"SMILES-Based 2D-QSAR and Similarity Search for Identification of Potential New Scaffolds for Development of SARS-CoV-2 MPRO Inhibitors","volume":"33","author":"Costa","year":"2022","journal-title":"Struct. Chem."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13321-017-0215-1","article-title":"ChemSAR: An Online Pipelining Platform for Molecular SAR Modeling","volume":"9","author":"Dong","year":"2017","journal-title":"J. Cheminform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107872","DOI":"10.1016\/j.compbiolchem.2023.107872","article-title":"Identification of Promising Inhibitory Heterocyclic Compounds against Acetylcholinesterase Using QSAR, ADMET, Biological Activity, and Molecular Docking","volume":"104","author":"Nguyen","year":"2023","journal-title":"Comput. Biol. Chem."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"153412","DOI":"10.1016\/j.tox.2022.153412","article-title":"Prediction of Drug-Induced Mitochondrial Dysfunction Using Succinate-Cytochrome c Reductase Activity, QSAR and Molecular Docking","volume":"485","author":"Moore","year":"2023","journal-title":"Toxicology"},{"key":"ref_46","first-page":"1","article-title":"Enhancement of Drug Discovery with Machine Learning Clustering Algorithms","volume":"6","author":"Damarla","year":"2022","journal-title":"J. High Sch. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.chemolab.2015.03.001","article-title":"Application of K-Means Clustering, Linear Discriminant Analysis and Multivariate Linear Regression for the Development of a Predictive QSAR Model on 5-Lipoxygenase Inhibitors","volume":"143","author":"Andrada","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1002\/jcc.23576","article-title":"QSARINS-chem: Insubria Datasets and New QSAR\/QSPR Models for Environmental Pollutants in QSARINS","volume":"35","author":"Gramatica","year":"2014","journal-title":"J. Comput. Chem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.ejmech.2013.10.029","article-title":"Comparison of Multiple Linear Regressions and Neural Networks Based QSAR Models for the Design of New Antitubercular Compounds","volume":"70","author":"Ventura","year":"2013","journal-title":"Eur. J. Med. Chem."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e07463","DOI":"10.1016\/j.heliyon.2021.e07463","article-title":"QSAR, Molecular Docking and ADMET Properties in Silico Studies of Novel 4,5,6,7-Tetrahydrobenzo[D]-Thiazol-2-Yl Derivatives Derived from Dimedone as Potent Anti-Tumor Agents through Inhibition of C-Met Receptor Tyrosine Kinase","volume":"7","author":"Daoui","year":"2021","journal-title":"Heliyon"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"10890","DOI":"10.1080\/07391102.2023.2259482","article-title":"In Silico Identification of Novel Heterocyclic Compounds Combats Alzheimer\u2019s Disease through Inhibition of Butyrylcholinesterase Enzymatic Activity","volume":"42","author":"Nguyen","year":"2024","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8806","DOI":"10.1038\/s41598-021-88341-1","article-title":"Support Vector Regression-Based QSAR Models for Prediction of Antioxidant Activity of Phenolic Compounds","volume":"11","author":"Shi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_54","unstructured":"(2023). Issue Information. WIREs Comput. Mol. Sci., 13, e1628."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13321-020-0416-x","article-title":"Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees","volume":"12","author":"Probst","year":"2020","journal-title":"J. Cheminform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13321-021-00506-2","article-title":"Analysis of the Effects of Related Fingerprints on Molecular Similarity Using an Eigenvalue Entropy Approach","volume":"13","author":"Kuwahara","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.yrtph.2018.11.002","article-title":"Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-across: Recommendations for Optimal Use","volume":"101","author":"Mellor","year":"2019","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"10457","DOI":"10.1038\/s41598-022-13532-3","article-title":"Artificial Neural Network Model with Different Backpropagation Algorithms and Meteorological Data for Solar Radiation Prediction","volume":"12","author":"Heng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1021\/ci050413p","article-title":"QSAR \u2212 How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets","volume":"46","author":"Gedeck","year":"2006","journal-title":"J. Chem. Inf. Model."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1111\/j.1747-0285.2006.00384.x","article-title":"PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching","volume":"67","author":"Dixon","year":"2006","journal-title":"Chem. Biol. Drug Des."},{"key":"ref_61","unstructured":"Schr\u00f6dinger, LLC (2023). Schr\u00f6dinger Release 2023-1, Schr\u00f6dinger, LLC. Available online: https:\/\/www.schrodinger.com\/."},{"key":"ref_62","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"56","DOI":"10.25080\/Majora-92bf1922-00a","article-title":"Data Structures for Statistical Computing in Python","volume":"445","author":"McKinney","year":"2010","journal-title":"Scipy"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"120","DOI":"10.2533\/chimia.2012.120","article-title":"From Theory to Bench Experiment by Computer-Assisted Drug Design","volume":"66","author":"Schneider","year":"2012","journal-title":"Chimia"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"W357","DOI":"10.1093\/nar\/gkz382","article-title":"SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules","volume":"47","author":"Daina","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: An Integrated Online Platform for Accurate and Comprehensive Predictions of ADMET Properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","article-title":"pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures","volume":"58","author":"Pires","year":"2015","journal-title":"J. Med. Chem."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1002\/jcc.27390","article-title":"MzDOCK: A Free Ready-to-Use GUI-Based Pipeline for Molecular Docking Simulations Simulations","volume":"45","author":"Kabier","year":"2024","journal-title":"J. Comput. Chem."},{"key":"ref_69","unstructured":"BIOVIA (2026, March 27). Dassault Syst\u00e8mes, Discovery Studio Modeling Environment, Release 2016, San Diego: Dassault Syst\u00e8mes. Available online: https:\/\/www.3ds.com\/products\/biovia\/discovery-studio."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4769","DOI":"10.1103\/PhysRevE.61.4769","article-title":"Nos\u00e9-Hoover Chain Method for Nonequilibrium Molecular Dynamics Simulation","volume":"61","year":"2000","journal-title":"Phys. Rev. E"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"103141","DOI":"10.1016\/j.jksus.2024.103141","article-title":"Effects of Heat Treatment on the Chemical Composition, Antioxidant Activity, and Toxicity of Agarwood Oil","volume":"36","author":"Syameera","year":"2024","journal-title":"J. King Saud Univ. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.molstruc.2017.09.056","article-title":"Synthesis, Spectroscopic, Computational (DFT\/B3LYP), AChE Inhibition and Antioxidant Studies of Imidazole Derivative","volume":"1151","author":"Ahmad","year":"2018","journal-title":"J. Mol. Struct."}],"container-title":["Pharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/4\/600\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:21:47Z","timestamp":1775794907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/4\/600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":72,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["ph19040600"],"URL":"https:\/\/doi.org\/10.3390\/ph19040600","relation":{},"ISSN":["1424-8247"],"issn-type":[{"value":"1424-8247","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]}}}