Journal of Computer-Aided Molecular Design最新文献

{"title":"MGCLSyn: multimodal graph contrastive learning for drug synergy prediction","authors":"Ying Yang,&nbsp;Jiancheng Ni,&nbsp;Cundong Lin,&nbsp;YanWei Wang,&nbsp;Chong Zhang","doi":"10.1007/s10822-026-00959-3","DOIUrl":"10.1007/s10822-026-00959-3","url":null,"abstract":"<div><p>Accurate prediction of drug synergy is critical for accelerating the discovery of effective combination therapies, particularly in precision oncology. However, many existing computational approaches rely on limited data modalities or employ simple fusion strategies, which restrict their ability to capture complex drug-cell interactions and limit generalisation performance. To address these challenges, we propose MGCLSyn, a multimodal graph contrastive learning framework for drug synergy prediction. MGCLSyn integrates multiple complementary data sources, including molecular graphs, drug similarity networks, fingerprint diffusion graphs, and cell-line gene expression profiles, to learn comprehensive drug and cellular representations. In addition, a cross-modal contrastive learning mechanism is introduced to align representations across different structural views, improving consistency and discriminative capability. Furthermore, a hierarchical interaction modelling module with adaptive fusion is designed to capture complex drug-drug and drug-cell relationships. Extensive experiments on two public benchmark datasets demonstrate that MGCLSyn consistently outperforms several state-of-the-art methods in terms of predictive performance and generalisation capability. These results suggest that integrating multimodal graph representations with contrastive alignment provides an effective approach for modelling drug synergy and may facilitate the development of precision combination therapies.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189938","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":"Computer-aided development of novel thiazolidinone–thiazole derivatives: multidimensional structure–activity relationships revealed by quantum chemical analysis, molecular docking and biological investigation","authors":"Tayyiaba Iqbal,&nbsp;Shoaib Khan,&nbsp;Asrar Khan,&nbsp;Hassnain Sajjad,&nbsp;Masi A. Almalki,&nbsp;Souhir Abid,&nbsp;Majdi Abid,&nbsp;Syed Kashif Ali,&nbsp;Zaripov Bakridin,&nbsp;Eshmuratov Baltabay,&nbsp;Sobhi M. Gomha,&nbsp;Magdi E. A. Zaki","doi":"10.1007/s10822-026-00962-8","DOIUrl":"10.1007/s10822-026-00962-8","url":null,"abstract":"<div><p>Diabetes mellitus is a major global health challenge, highlighting the need for new inhibitors of the carbohydrate-hydrolyzing enzymes α-amylase and α-glucosidase. In this study, a series of thiazole-based thiazolidinone derivatives (1–10) was designed, synthesized, and structurally characterized using spectroscopic techniques. Among the synthesized compounds, 8 showed the strongest inhibitory activity against α-amylase and α-glucosidase, with IC₅₀ values of 5.50 ± 0.40 and 5.90 ± 0.10 µM, respectively, and compared with 10.80 ± 0.50 and 11.10 ± 0.30 µM for acarbose. Molecular docking suggested that compound 8 can adopt a favorable binding orientation within the active sites of both enzymes through hydrogen-bonding and hydrophobic contacts. Enzyme kinetic studies further characterized its mode of inhibition. DFT calculations were performed to examine the electronic properties, frontier molecular orbitals, and molecular electrostatic potential of selected derivatives. ADMET analysis was also conducted to provide preliminary insight into their predicted pharmacokinetic and toxicity-related properties. Overall, the experimental and computational findings identify compound 8 as a promising lead structure for further investigation as an α-amylase and α-glucosidase inhibitor.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189939","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":"Exploring the pharmacological basis of adverse effects in oxazolidinone and antidepressant cotherapy: an in silico study","authors":"Gael García-Valdez,&nbsp;Luis Fernando Méndez-López,&nbsp;Set Mondragon-Gutiérrez,&nbsp;Daniela Treviño-Almaguer,&nbsp;María del Rayo Camacho-Corona,&nbsp;Bryan Alejandro Espinosa-Rodríguez","doi":"10.1007/s10822-026-00964-6","DOIUrl":"10.1007/s10822-026-00964-6","url":null,"abstract":"<div><p>Linezolid and related oxazolidinones are frontline antibiotics against multidrug-resistant Gram-positive pathogens, including methicillin-resistant <i>Staphylococcus aureus</i> (MRSA), yet their co-administration with antidepressants has been clinically associated with serotonin syndrome (SS) or hypertensive crisis (HC). While traditionally attributed to monoamine oxidase (MAO) inhibition, clinical observations remain variable and the underlying mechanism is not completely understood. In this exploratory study, we applied an integrated in silico framework combining target prediction, network similarity, and structural modeling to generate testable hypotheses regarding potential alternative targets. Consensus profiling using SwissTargetPrediction and an integrative structural- and physicochemical-based target prediction methodology predicted possible interactions of linezolid and tedizolid with MAO isoforms, alongside exploratory aminergic targets including the 5-HT1A (HTR1A) receptor and adrenergic clusters. Comparing predicted targets with clinical datasets revealed an overlap with genes associated with SS and HC. Pathway enrichment and Jaccard similarity showed notable functional convergence with sertraline (OC &gt; 0.90). Structural comparisons and physicochemical clustering grouped oxazolidinones near neurotransmitter intermediates, such as N-acetylserotonin. Furthermore, molecular docking across multiple crystallographic structures and ligand efficiency (LE) estimations suggested theoretically favorable interactions within MAO and HTR1A binding pockets, supported by shared pharmacophoric features. These computational findings provide a preliminary, hypothesis-generating basis suggesting that oxazolidinone-induced toxicity may involve targets beyond MAO, highlighting HTR1A modulation as a candidate mechanism requiring dedicated in vitro and in vivo validation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189940","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":"Structure-guided discovery of a dual JAK2/RAGE modulator reveals a computationally driven strategy for targeting convergent oncogenic signaling in renal cell carcinoma","authors":"Mesfer Mohammad-Al-Shahrani,&nbsp;Mohammad Abohassan,&nbsp;Prasanna Rajagopalan","doi":"10.1007/s10822-026-00960-w","DOIUrl":"10.1007/s10822-026-00960-w","url":null,"abstract":"<div><p>Renal cell carcinoma (RCC) is driven by convergent inflammatory and proliferative signaling pathways. RAGE-mediated NF-κB activation and JAK2–STAT3 signaling cooperate to amplify cytokine production and oncogenic progression. Targeting these interconnected pathways represents a rational therapeutic strategy. High-throughput virtual screening of ChemBridge libraries, 100 ns molecular dynamics simulations, and MM/PBSA analysis were performed. The lead compound RJS6641 was evaluated using biochemical assays (HTRF JAK2 kinase assay, differential scanning fluorimetry), a cell-free AGE–RAGE binding ELISA, and functional assays in RCC cell lines, including viability, reporter, cytokine, apoptosis, and migration analyses. RJS6641 showed stable binding to JAK2 (protein RMSD ≈0.28 nm; ligand RMSD 0.15–0.20 nm) and RAGE (protein RMSD 0.50–1.50 nm; ligand RMSD 0.30–0.35 nm). It inhibited JAK2 in an ATP-competitive manner (IC₅₀ = 60.23 nM; ruxolitinib IC₅₀ = 1.69 nM), with ATP-dependent shifts (35.04 → 74.09 → 305.6 nM) and thermal stabilization (ΔTm +1.50 °C). AGE–RAGE binding was disrupted (IC₅₀ = 118.2 nM). RJS6641 reduced RCC viability (GI₅₀: 799.8–2021 nM) while sparing HK-2 cells (GI₅₀ 12,816 nM; 6–16× difference). It suppressed STAT3 (IC₅₀ 412.3–813.2 nM) and NF-κB (IC₅₀ 435.7–1031 nM) signaling, reduced IL-6/IL-8 secretion, induced caspase−3/7 activation (EC₅₀ 662.4–862.9 nM), and inhibited migration (up to 83%). Collectively, RJS6641 functions as a JAK2 inhibitor with additional modulation of RAGE-associated signaling, leading to coordinated suppression of STAT3/NF-κB pathways and anti-tumor effects in RCC, supporting further in vivo and selectivity evaluation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189572","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":"Toward FOXM1 inhibitors based on supervised machine learning classification models and similarity-based screening","authors":"Ashutosh Kharwar,&nbsp;Alberto Marbán-González,&nbsp;José L. Medina-Franco,&nbsp;Carlos A. Velázquez-Martínez","doi":"10.1007/s10822-026-00952-w","DOIUrl":"10.1007/s10822-026-00952-w","url":null,"abstract":"<div><p>Ovarian cancer remains among the deadliest gynaecological malignancies, with aberrant activation of the Forkhead Box M1 (FOXM1) transcription factor strongly implicated in tumour progression, therapy resistance, and poor prognosis. Given the limited availability of validated FOXM1 inhibitors and the lack of prior dedicated machine learning-based QSAR studies identified in our literature survey, this study employed a supervised machine learning framework to predict the bioactivity of structurally diverse small-molecule inhibitors. A curated dataset of 101 compounds with reported IC₅₀ values was obtained from the ChEMBL database, and 1444 molecular descriptors were computed and filtered to retain 43 non-redundant features for modelling. Seven regression models, namely multiple linear regression, Ridge, partial least squares, Random Forest, Elastic Net, k-nearest neighbour, and support vector regression, were developed and assessed using training-set fitting, nested cross-validation, and external test-set validation. Among the evaluated models, Random Forest showed the strongest overall external predictive performance, achieving R²_external = 0.797, RMSE = 0.333, MAE = 0.250, Q²F₁ = 0.801, Q²F₂ = 0.797, Q²F₃ = 0.785, CCC = 0.875, and MRE = 6.00%, while satisfying the Golbraikh–Tropsha Criterion A. The k-nearest neighbour model also showed strong predictive ability, with R²_external = 0.781 and the highest nested cross-validated performance (Q²_CV_Nested = 0.728). SHAP analysis indicated that hydrophobic aliphatic features, electronic distribution, molecular rigidity, and hydrogen-bonding related descriptors contribute meaningfully to FOXM1 inhibition. Structural similarity analysis of the five most active compounds using ECFP4 fingerprints and the Tanimoto coefficient revealed substantial scaffold conservation, with similarity values ranging from approximately 64.71% to 99.42%. Bemis–Murcko scaffold analysis identified 59 unique scaffolds from 101 compounds, indicating moderate scaffold diversity, while PCA and t-SNE showed that active compounds occupy defined regions of chemical space. These findings provide an interpretable ML-QSAR framework for FOXM1 inhibitors and a useful computational basis for analysing and prioritizing compounds within the studied chemical space.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132720","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":"Computer-aided molecular engineering of novel imidazolone–chalcone conjugates as antidiabetic agent: synthesis, molecular docking, DFT study, enzyme kinetics and ADME profiling","authors":"Tayyiaba Iqbal,&nbsp;Sampath Chinnam,&nbsp;Iftikhar Hussain,&nbsp;Masi A. Almalki,&nbsp;Sobhi M. Gomha,&nbsp;Magdi E. A. Zaki,&nbsp;Khasan Kayumov,&nbsp;Aftab Ahmad Khan,&nbsp;Rania H. Taha,&nbsp;Faoz H. Al-Atawi,&nbsp;Ahmad Irfan,&nbsp;Shoaib Khan","doi":"10.1007/s10822-026-00951-x","DOIUrl":"10.1007/s10822-026-00951-x","url":null,"abstract":"<div><p>This study aimed to identify potent drug candidates against diabetes mellitus (DM), particularly through inhibition of the key carbohydrate-hydrolyzing enzymes α-amylase and α-glucosidase. For this purpose, a series of novel imidazolone-based chalcone derivatives was synthesized through an efficient and rapid synthetic route. The synthesized analogues were initially monitored and confirmed by thin-layer chromatography (TLC). Their structural characteristics were established using ¹H NMR and ¹³C NMR spectroscopy, while high-resolution electron ionization mass spectrometry (HREI-MS) was employed to determine their accurate molecular masses. The synthesized analogues were evaluated for their inhibitory potential against α-amylase and α-glucosidase, and their activities were compared with the standard inhibitor acarbose. Among the synthesized compounds, analogue 5 exhibited the highest inhibitory activity, with IC₅₀ values of 6.80 ± 0.10 µM against α-amylase and 7.10 ± 0.30 µM against α-glucosidase. Enzyme kinetic studies were subsequently performed at different substrate and inhibitor concentrations to investigate the mode and mechanism of enzyme inhibition. To further elucidate the protein–ligand interactions, the most potent analogues were subjected to molecular docking and pharmacophore modeling, which provided insights into their binding orientations and key interactions with active-site amino acid residues. Molecular dynamics (MD) simulations were additionally performed to investigate the stability and dynamic behavior of the selected protein–ligand complexes. Furthermore, density functional theory (DFT) calculations and ADMET analyses were conducted to examine the electronic properties, molecular electrostatic potential, and drug-like and pharmacokinetic characteristics of the potent analogues. Collectively, these experimental and computational investigations provided comprehensive insights into the antidiabetic potential and molecular characteristics of the synthesized imidazolone-based chalcone derivatives.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132689","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":"DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction","authors":"Deepak Devakumar Sagayaraj,&nbsp;Priya Dharshini Balaji,&nbsp;Arvin Anand,&nbsp;Suriya Murugavel,&nbsp;Subathra Selvam,&nbsp;Thirumurthy Madhavan","doi":"10.1007/s10822-026-00956-6","DOIUrl":"10.1007/s10822-026-00956-6","url":null,"abstract":"<div><p>Cancer remains one of the leading causes of mortality worldwide, and the discovery of novel anticancer drugs continues to be a costly and time-consuming process. Although machine learning (ML) and deep learning (DL) have significantly advanced computational drug discovery, many existing predictive models rely on a single learning algorithm and often fail to generalize across diverse molecular descriptors, limiting their predictive reliability. In this study, we propose an interpretable hybrid ML-DL ensemble framework for the prediction of anticancer small molecules by integrating complementary learning algorithms, multiple feature selection strategies, and ensemble fusion techniques to improve robustness, generalization, and recall-oriented prediction performance. A total of 102 ML models were developed using 17 algorithms on two molecular descriptor datasets (2D and Hybrid) with three feature selection strategies, while 12 DL models based on Improved Deep MLP, FT-Transformer, and TabNet architectures were optimized using five-fold cross-validation. Out-of-fold predictions from the optimized ML and DL models were further integrated through eight ensemble strategies to construct ML, DL, and hybrid ML-DL ensembles. The best-performing hybrid ensemble, comprising XGBoost, Improved Deep MLP, and TabNet trained on the 2D dataset with mutual information-selected features, achieved a macro-recall of 0.83, an accuracy of 0.83, and a ROC-AUC of 0.90, outperforming the individual ML and DL models. Our developed model correctly identified 8 out of 10 FDA-approved compounds with known anticancer and non-anticancer labels during external validation. Additionally, the framework was applied to 1200 FDA-approved drugs from the ZINC database for prospective virtual screening, where compounds were ranked according to their ensemble prediction probabilities to prioritize. These findings indicate that integrating complementary ML and DL models with ensemble decision fusion enables more accurate and reliable identification of anticancer small molecules than individual predictive models. Overall, the proposed framework provides a reproducible, interpretable, and scalable computational strategy for accelerating early-stage anticancer drug discovery.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132834","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":"Automated pharmacophore hypothesis generator for SILCS-Pharm: ph4gen","authors":"Erik B. Nordquist,&nbsp;Alexander D. MacKerell Jr.","doi":"10.1007/s10822-026-00957-5","DOIUrl":"10.1007/s10822-026-00957-5","url":null,"abstract":"<div><p>Here we present ph4gen, a tool for the automated generation of pharmacophore hypotheses developed for use with the site identification by ligand competitive saturation (SILCS) technology. It builds upon the previously established SILCS-Pharm approach which generates the pharmacophore features from a given macromolecular structure, and SILCS-Hotspots which identifies potentially-druggable binding sites. The tool examines a set of pharmacophore features, typically 15–20, and ranks all permutations of hypotheses of a desired length, with default of 4 to 5 features per hypothesis. A diverse data set of seven proteins (namely, BACE1, CDK2, JNK1, P38, PTPT1B, Thrombin, and TYK2) and 336 ligands was used to train the logistic regression model which ranks these hypotheses and recalls at least one experimental pharmacophore hypothesis in four of seven cases in the top 10. The model was tested on a set of four separate proteins (namely FXR, HDM2, HSP90, and TRMD) and 354 ligands and recalls at least one experimental pharmacophore hypothesis in the top 5 in all four test systems, with mean precision and recall of 0.23 ± 0.06 and 0.15 ± 0.04, respectively. We show that the distribution of hypotheses generated is similar to that of the validated hypotheses, and that even in cases of near-misses, the pharmacophore hypotheses have similar chemical features to validated ones. The tool can also be tuned to provide greater or lesser chemical diversity when utilized in virtual screening. Ultimately, ph4gen is a useful tool as part of the SILCS workflow to identify druggable binding sites, generate drug-like pharmacophore hypotheses, and rank in silico hits for experimental validation.</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":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149102630","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":"Identification of new ER-targeted anticancer hits active against MCF-7 breast cancer cells in the marine fungal secondary metabolite library using computative and experimental approaches","authors":"Ekaterina A. Yurchenko,&nbsp;Liliana E. Nesterenko,&nbsp;Leonid A. Kaluzhskiy,&nbsp;Evgeniy O. Yablokov,&nbsp;Ekaterina A. Chingizova,&nbsp;Ekaterina A. Lesovaya,&nbsp;Alexis S. Ivanov,&nbsp;Dmitry L. Aminin,&nbsp;Anton N. Yurchenko","doi":"10.1007/s10822-026-00953-9","DOIUrl":"10.1007/s10822-026-00953-9","url":null,"abstract":"<div><p>The identification of novel estrogen receptor (ER)-targeted anticancer compounds from marine fungal secondary metabolites was performed using a combined computational and experimental approach. A library of 461 marine fungal metabolites was analyzed using chemical space visualization, structural similarity assessment using Tanimoto indices, and target-prediction tools to identify potential ER ligands. Scaffold analysis of predicted hits suggested decalin/bicyclic terpenoid (6–6), tricyclic 6-6-6 terpenoid, bicyclic 5–6 system and several polycyclic system chemotypes as novel for ER ligands. The ER-dependence of the proposed hits cytotoxicity was tested in vitro against ER-positive human breast cancer MCF-7 cells using a 4-hydroxytamoxifen (4-OHT)-competitive viability assay. The compounds virescenoside M, cladosporin, zosteropenilline S, and griseofulvin exhibited ER-dependent cytotoxic effects on MCF-7 cells, confirming the in silico predictions. In 48 h experiments, cladosporin, zosteropenilline S, and griseofulvin at 100 µM decreased the MCF-7 cell viability by 73.6%, 33.2%, and 45.8%, respectively, but statistically significant cytotoxic effect of griseofulvin also was observed at its concentration of 1 µM. Surface plasmon resonance (SPR) experiments confirmed the direct binding of griseofulvin to ERα, with a dissociation constant (Kd) of 8.00 µM. These findings highlight marine fungi as a valuable source of novel ER-targeted anticancer agents and suggest a novel ER-dependent mechanism for the antifungal drug griseofulvin, providing a basis for its potential repositioning in ER-positive breast cancer therapy. This integrated computational-experimental strategy provides a framework for discovering new lead compounds for breast cancer treatment.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149102661","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":"Are QM/MM models systematically improvable? A case study on carbonic anhydrase binding affinities","authors":"Haedam Mun,&nbsp;Junming Ho","doi":"10.1007/s10822-026-00954-8","DOIUrl":"10.1007/s10822-026-00954-8","url":null,"abstract":"<div><p>Hybrid quantum mechanics/molecular mechanics (QM/MM) is the method of choice for modelling large biochemical systems, but their performance depends critically on how they are implemented. This paper examines the sensitivity of QM/MM enzyme-ligand binding energy predictions to key parameters, including QM region size, the incorporation of thermal and solvation effects as well the use of docked poses versus crystal structures. The focus is on carbonic anhydrase which has emerged as an important drug target for a wide variety of pathologies. It is demonstrated that trends in binding affinities are surprisingly insensitive with respect to these parameters. QM/MM geometry optimization of poses obtained from molecular docking enhances predictive utility for yet-to-be synthesized ligands, though outliers arise from structural deviations or non-native interactions. These findings provide practical guidelines for QM/MM applications in carbonic anhydrase and similar metalloenzymes. </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":"40 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-026-00954-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149102614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}