Journal of Computer-Aided Molecular Design最新文献

{"title":"Predicting drug-target affinity through triple pre-activated random residual planet convolution coupled attention network and contact maps.","authors":"M Sudha, B Senthilnayaki, K Padmanaban, L Guganathan","doi":"10.1007/s10822-025-00667-4","DOIUrl":"https://doi.org/10.1007/s10822-025-00667-4","url":null,"abstract":"<p><p>Drug discovery relies on the ability to predict drug-target affinity (DTA), which allows for the efficient identification of drug candidates for certain protein targets. Scalability, accuracy, and interpretability are issues that traditional methods must deal with. In order to improve prediction accuracy, this study proposes a sophisticated approach that combines contact map representations with the Triple Pre-Activated Random Residual Planet Convolution Attention Network (Tri-Pre-A2RP-2CAN). The DTA, KIBA, and Davis datasets are the sources of the input data. Preprocessing employs Focal Vision Transformer with a Gabor Filter for feature enhancement. Feature extraction uses a Dual-Aggregation Transformer (DAT) to capture complex molecular and protein patterns. The modeling framework incorporates Tri-Pre-A2RP-2CAN and RCNN, optimized with PACRTAMN architecture and Planet optimization based hyperparameter tuning. This innovative approach achieves 99.9% accuracy, outperforming existing methods in modeling drug-target interactions. It enhances DTA prediction, improves molecular interaction analysis, and optimizes drug discovery processes, offering scalable and interpretable solutions for pharmaceutical advancements.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":"91"},"PeriodicalIF":3.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.","authors":"Prashanth Choppara, Lokesh Bommareddy","doi":"10.1007/s10822-025-00669-2","DOIUrl":"https://doi.org/10.1007/s10822-025-00669-2","url":null,"abstract":"<p><p>Protein pockets, or small cavities on the protein surface, are critical sites for enzymatic catalysis, molecular recognition, and drug binding. Accurately identifying these pockets is crucial for understanding protein function and designing therapeutic interventions. Traditional computational methods such as molecular docking, surface grid mapping, and molecular dynamics simulations are hampered by the use of fixed protein structures, and therefore it is challenging to identify cryptic pockets when they appear under physiological conditions. We propose a deep reinforcement learning (DRL) technique based on deep Q-networks (DQN) to identify precise protein pockets. Our strategy to improve the prediction of functional binding sites incorporates important molecular descriptors such as spatial coordinates, solvent-accessible surface area (SASA), hydrophobicity, and electrostatic charge. We pre-process protein structure data from the protein data bank (PDB) through feature extraction and selection methods, including variance threshold filtering and dimensionality reduction using an autoencoder. The sparse feature representation enables efficient training of a DQN agent, which navigates protein surfaces and iteratively optimizes pocket predictions. By using reinforcement learning concepts, the model adapts its pocket detection strategy according to the learned reward signals, increasing sensitivity and specificity. The method is tested on benchmark datasets and is found to exhibit superior performance in detecting well-defined and cryptic pockets over traditional computational methods. Experimental evidence suggests that our model successfully identifies binding sites in various protein families, with significant implications for drug discovery and protein-ligand interaction studies. Moreover, the model's ability to incorporate geometric and biochemical features allows for a better understanding of pocket functionality. The scalability of our method makes it an important tool for large-scale virtual screening and personalized medicine. By using deep reinforcement learning, this research provides a new and effective framework for protein pocket prediction, opening up opportunities for developing new tools in structural bioinformatics, drug design, and molecular biology research.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":"90"},"PeriodicalIF":3.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a predictive QSAR model for FGFR-1 inhibitors: integrating computational and experimental validation.","authors":"Sandip D Nagare, Sharav A Desai, Vipul P Patel, Siddhi Sapkal, Madhulika More, Aditi Kate, Aliasgar F Shahiwala, Tanmaykumar Varma, Prabha Garg","doi":"10.1007/s10822-025-00671-8","DOIUrl":"https://doi.org/10.1007/s10822-025-00671-8","url":null,"abstract":"<p><p>The traditional drug discovery process is often lengthy, costly, and characterized by a high failure rate. There is a pressing need for innovative strategies to optimize this process and improve the chances of identifying effective therapeutic candidates. This study aims to utilize computational methods to develop a quantitative structure-activity relationship (QSAR) model that predicts the inhibitory activity of compounds against Fibroblast Growth Factor Receptor 1 (FGFR-1), which is associated with various cancers, including lung and breast cancer. The QSAR model was developed using multiple linear regression (MLR) on a dataset of 1779 compounds from the ChEMBL database. The dataset was curated, and molecular descriptors were calculated using Alvadesc software. Feature selection techniques refined the dataset, and the model's predictive capability was validated through 10-fold cross-validation and external validation with a test set. In silico validation was further performed using molecular docking and molecular dynamics simulations. Additionally, in vitro validation was conducted using MTT, wound healing, and clonogenic assays on A549 (lung cancer), MCF-7 (breast cancer), HEK-293 (normal human embryonic kidney), and VERO (normal African green monkey kidney) cell lines. The QSAR model exhibited strong predictive performance with an R² value of 0.7869 for the training set and 0.7413 for the test set. Molecular docking and dynamics simulations further supported the model's predictions, demonstrating stable interactions between the compounds and FGFR-1. Experimental validation through the MTT assay revealed a significant correlation between predicted and observed pIC50 values, confirming the model's accuracy. Oleic acid, identified as the most promising compound, showed substantial inhibitory effects on A549 and MCF-7 cells, with low cytotoxicity observed on normal cell lines. The integration of computational and experimental methods significantly enhanced the efficiency and accuracy of the drug discovery process for FGFR-1 inhibitors.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":"89"},"PeriodicalIF":3.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational and experimental studies to discover a promising lead compound, chemically related to natural acetylene acetogenins from Porcelia macrocarpa, against amastigotes of Leishmania (L.) infantum.","authors":"João Pedro P Encide, Ivanildo A de Brito, Maiara Amaral, Andre G Tempone, João Henrique G Lago, Kathia M Honorio","doi":"10.1007/s10822-025-00659-4","DOIUrl":"https://doi.org/10.1007/s10822-025-00659-4","url":null,"abstract":"<p><p>Previous studies of the natural acetylenic acetogenin (2S,3R,4R)-3-hydroxy-4-methyl-2-(eicos-11'-yn-19'-enyl)butanolide (1), isolated from the plant Porcelia macrocarpa, indicated its in vitro activity against the clinically relevant form of Leishmania (L.) infantum, the intracellular amastigotes and no mammalian cytotoxicity. A second chemically related acetogenin, (2S,3R,4R)-3-hydroxy-4-methyl-2-(eicos-11'-ynyl) butanolide (2), exhibited a lack of antileishmanial activity at the highest tested concentration of 150 µM. These results suggest that the terminal double bond plays a crucial role in the antileishmanial activity of these compounds. Using a computational protocol to predict the metabolism of 1, the 19'-oxirane-derivative (3) was proposed, prepared, and experimentally tested against Leishmania (L.) infantum amastigotes. Compound 3 presented twofold more potency than 1, with an EC₅₀ value of 11.3 µM. Compounds 1-3 were also analyzed via molecular docking against L. (L.) infantum trypanothione reductase (TR) and thiol-dependent reductase 1 (TDR1), showing that the natural products 1 and 2 prefer specific regions in the active sites for lactone positioning. Docking of derivative 3 revealed interaction patterns between the different acetogenins, with the lactone moieties positioned in the same regions as compounds 1 and 2. Therefore, in silico prediction of metabolites from bioactive ligands can contribute to the design of potent derivatives, as demonstrated in this study, which aligns with our experimental findings.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":"88"},"PeriodicalIF":3.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular dynamics modeling and spectroscopic property prediction of V-type nerve agents for safe handling","authors":"Koufou Antonios,&nbsp;Chalaris Michail","doi":"10.1007/s10822-025-00668-3","DOIUrl":"10.1007/s10822-025-00668-3","url":null,"abstract":"<div><p>Detailed molecular potential models of three major representative substances of V type agents were created and tested against the scarce available experimental results. Molecular Dynamics simulations were conducted, and first main focus was elucidating thermodynamic and transport properties of these highly toxic organophosphorus compounds. Alongside, an in-depth investigation of their intermolecular structure and vibrational spectra calculations were performed. Using classical simulations key thermodynamic quantities such as density, enthalpy of vaporization, heat capacity under constant pressure as well as transport properties such as viscosity and self-diffusion coefficient were computed. Molecular level structural organization was probed through pair radial distribution functions, providing insight into short range interactions and ordering of molecular sites and atoms, as well as coordination numbers. Furthermore, infrared spectra concerning vibrational states were derived from inverse Fourier transform of the total dipole autocorrelation function, revealing signature vibrational modes in the infrared fingerprint region for functional group identification. This combined approach offers a critical molecular insight into the behavior of V type chemical warfare agents under ambient conditions, contributing to predictive modelling and safe handling of these hazardous substances. This study presents the first comprehensive atomistic simulation of VX, RVX, and CVX, offering detailed thermodynamic, transport, and spectroscopic insights through refined OPLS-based potential models.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles","authors":"Alexey Ereshchenko,&nbsp;Sergei Evteev,&nbsp;Alexander Malyshev,&nbsp;Denis Adjugim,&nbsp;Fedor Sizov,&nbsp;Anna Pastukhova,&nbsp;Victor Terentiev,&nbsp;Petr Shegai,&nbsp;Andrey Kaprin,&nbsp;Yan Ivanenkov","doi":"10.1007/s10822-025-00666-5","DOIUrl":"10.1007/s10822-025-00666-5","url":null,"abstract":"<div><p>Kinase profiling is an essential step in both hit identification and selectivity evaluation. Since in vitro testing of large chemical libraries is costly and time-consuming, a computational approach can be applied to narrow down the reasonable chemical space. In this work, we collected data from several sources and prepared a curated, comprehensive database for training machine learning (ML) models to predict selectivity towards 75 kinases. We demonstrated the usefulness of this database by preparing several ML models with various molecular representations and model architectures. Among these, a graph neural network-based model enhanced by utilizing 3D pharmacophore ensembles showed the best performance. Finally, the developed model was applied to a library of in-stock compounds to facilitate kinase-focused drug discovery.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DFT-based investigation of chitin-to-chitosan transition: effects of N-acetylation on structure and reactivity","authors":"Rodolfo Daniel Ávila-Avilés","doi":"10.1007/s10822-025-00651-y","DOIUrl":"10.1007/s10822-025-00651-y","url":null,"abstract":"<div><p>The degree and pattern of deacetylation in chitin-derived polymers critically determine their physicochemical properties and functional potential. In this study, a comprehensive theoretical analysis is performed of decameric chitin-like chains with systematically varied degrees of deacetylation (DDA), using density functional theory (DFT), electrostatic surface mapping, noncovalent interaction (NCI) analysis, and global reactivity descriptors. Structural optimizations revealed that partial deacetylation induces significant torsional rearrangements and enhanced intra-chain hydrogen bonding, leading to increased conformational flexibility. Molecular electrostatic potential (MEP) surfaces demonstrated a transition from neutral, acetyl-dominated topologies to highly polarized and reactive amine-rich domains. NCI analysis confirmed the emergence of cooperative hydrogen bonding and van der Waals networks in mid-range DDA structures. Furthermore, HOMO–LUMO analysis and TAFF-derived descriptors identified 20% ([[GlcNac]₄- GlcN]₂)–60% ([[GlcN]₃-[GlcNac]₂]₂) DDA chains as electronically soft, highly polarizable, and capable of dual electron donation and acceptance. These findings suggest that partially deacetylated chitosan chains exhibit a unique combination of flexibility, reactivity, and internal cohesion, providing a molecular rationale for their superior performance in biomedical and functional materials applications.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated strategy for screening direct Keap1-Nrf2 PPI inhibitors from traditional Chinese medicine: a case study of Achyranthis bidentatae Radix","authors":"Ban Chen,&nbsp;Shuangshuang Liu,&nbsp;Huiyin Xia,&nbsp;Xican Li,&nbsp;Rongxin Cai,&nbsp;Yingqing Zhang,&nbsp;Yuchen Hu,&nbsp;Jiangtao Su","doi":"10.1007/s10822-025-00662-9","DOIUrl":"10.1007/s10822-025-00662-9","url":null,"abstract":"<div><p>Direct inhibition of the Kelch-like ECH-associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor 2 (Nrf2) protein–protein interaction (PPI) represents a critical pathway for enhancing the antioxidant response. Therefore, screening for direct Keap1-Nrf2 PPI inhibitors holds significant potential for addressing oxidative stress-related diseases. This study aims to develop an integrated approach to identify direct Keap1-Nrf2 PPI inhibitors from traditional Chinese medicine (TCM) using <i>Achyranthis bidentatae Radix</i> (ABR) as a case study. The approach incorporated ultrahigh-performance liquid chromatography-quadrupole-orbitrap mass spectrometry analysis, data mining, drug-like property evaluation, molecular docking, chemical structure clustering, molecular dynamics (MD) simulations, in vitro experimental validation, and density functional theory (DFT) calculations. A total of 517 compounds were identified in ABR, of which 248 met the drug-likeness criteria. Additionally, seventeen compounds from six structural clusters were identified as having theoretical Keap1-Nrf2 PPI inhibitory activity. Among these compounds, shidasterone, nortrachelogenin, wogonin, and <i>N</i>-<i>trans</i>-feruloylmethoxytyramine were subjected to experimental evaluation for their Keap1-Nrf2 PPI inhibitory and free radical scavenging activities. MD simulations and DFT calculations demonstrated that these compounds directly inhibited Keap1-Nrf2 PPI through hydrophobic interactions, hydrogen bonds, and salt bridges. Moreover, DFT calculations confirmed that these compounds scavenged free radicals via the hydrogen atom transfer mechanism. In conclusion, the strategy presented herein offers a robust framework for screening direct Keap1-Nrf2 PPI inhibitors with structural diversity from ABR and other TCM sources.</p><h3>Graphical abstract</h3><p>An integrated strategy was developed to screen direct Keap1-Nrf2 PPI inhibitors from TCM taking <i>Achyranthis bidentatae Radix</i> as an example.</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assembling of phenyl substituted halogens in the C3-position of substituted isatins by mono wave assisted synthesis: development of a new class of monoamine oxidase inhibitors","authors":"Della Grace Thomas Parambi,&nbsp;Stephanus J. Cloete,&nbsp;Sunil Kumar,&nbsp;Tariq Ghazi Alsahli,&nbsp;Arafa Musa,&nbsp;Sumera Qasim,&nbsp;Muzammil Kabier,&nbsp;Sachithra Thazhathuveedu Sudevan,&nbsp;Saranya Kattil Parmbil,&nbsp;Anél Petzer,&nbsp;Jacobus P. Petzer,&nbsp;Bijo Mathew","doi":"10.1007/s10822-025-00663-8","DOIUrl":"10.1007/s10822-025-00663-8","url":null,"abstract":"<div><p>A series of ten chloro- and bromo-substituted isatin derivatives were synthesized and evaluated for their ability to inhibit the monoamine oxidase (MAO) enzymes. All compounds demonstrated more potent inhibition of MAO-A compared to MAO-B. The most potent MAO-A inhibitor was <b>HIB2</b> (IC₅₀ = 0.037 μM), followed by <b>HIB4</b> (IC₅₀ = 0.039 μM), while <b>HIB10</b> (IC₅₀ = 0.125 μM) exhibited the most potent inhibition of MAO-B. <b>HIB2</b> was identified as a specific MAO inhibitor with a selectivity index of 29 for MAO-A over MAO-B. The enzyme-inhibitor dissociation constants (K_i) for <b>HIB2</b> and <b>HIB10</b> were 0.031 μM and 0.036 μM, respectively, for MAO-A and MAO-B. Both <b>HIB2</b> and <b>HIB10</b> exhibited competitive and reversible inhibition. An analysis of the ADMET and PAMPA suggested that <b>HIB2</b> is permeable to the blood–brain barrier (BBB). Molecular docking analysis revealed that <b>HIB2</b> forms stable hydrogen bonds with Asn181 and Gln215 in the MAO-A ligand–protein complex. Dynamic analysis indicated the stability of <b>HIB2</b> with MAO-A. These findings suggest that <b>HIB2</b> is potent reversible MAO-A inhibitor, making this class of compounds potential therapeutic agents for neurological disorders.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shifting the paradigm of diabetes mellitus therapeutics: synthesis of novel fused pyrrolo-Imidazolidinone derivatives and their kinetic and computational profiling","authors":"Shoaib Khan,&nbsp;Tayyiaba Iqbal,&nbsp;Eman Alzahrani,&nbsp;Faez Falah Alshehri,&nbsp;Zafer Saad Al Shehri,&nbsp;Sobhi M. Gomha,&nbsp;Magdi E. A. Zaki,&nbsp;Hamdy Kashtoh","doi":"10.1007/s10822-025-00660-x","DOIUrl":"10.1007/s10822-025-00660-x","url":null,"abstract":"<div><p>Diabetes mellitus remains a major global health challenge, necessitating the search for potent and safer therapeutic agents. In this study, a series of novel pyrrolo-imidazolidinone derivatives (<b>1–10</b>) was designed and synthesized as potential anti-diabetic agents. Structural elucidation was carried out using HREI-MS, ¹H-NMR and ¹³C-NMR spectroscopy. The anti-diabetic potential of the compounds was evaluated in vitro against α-amylase and α-glucosidase enzymes. Among the synthesized derivatives, compounds <b>4</b>,<b> 5</b>,<b> and 7</b> exhibited the most potent inhibitory activity, with IC₅₀ valuesranging between 4.10 ± 0.30 to 2.10 ± 0.10 µM (α-amylase) and 4.80 ± 0.40 to 2.60 ± 0.20 µM (α-glucosidase), surpassing the reference drug acarbose (IC₅₀ = 4.20 ± 0.60 µM and 5.10 ± 0.10 µM, respectively). In silico studies, including molecular docking, pharmacophore modeling, and ADMET profiling, supported the experimental findings and provided insights into the structural features governing enzyme inhibition and drug-likeness. The results highlight pyrrolo-imidazolidinone derivatives as promising scaffolds for further development of effective anti-glycemic agents.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}