SAR and QSAR in Environmental Research最新文献_第3页

Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer. 整合机器学习和药物基因组学，用于卵巢癌潜在候选药物的生物标志物发现、鉴定和优先排序。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-05-01 Epub Date: 2026-06-08 DOI: 10.1080/1062936X.2026.2667960

S Yadav, A C Kaushik, G Srivastava, M I Siddiqi

{"title":"Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer.","authors":"S Yadav, A C Kaushik, G Srivastava, M I Siddiqi","doi":"10.1080/1062936X.2026.2667960","DOIUrl":"10.1080/1062936X.2026.2667960","url":null,"abstract":"Ovarian cancer remains a major global health concern and leading cause of mortality among women due to late diagnosis, therapeutic resistance, and limited predictive biomarkers for treatment response. There is an urgent need for integrative approaches to improve early detection and treatment outcomes. In this study, we integrated machine learning and pharmacogenomics to identify drug-sensitive biomarkers and prioritize therapeutic candidates in ovarian cancer. Pharmacogenomic data were obtained from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC-v2), including gene expression and drug response profiles across ovarian cancer cell lines. Predictive models were developed for six FDA-approved drugs using Elastic Net, Ridge, and Lasso regression, demonstrating robust predictive performance achieving Pearson correlation (r) to 0.65 and Spearman correlation (ρ) to 0.63 on validation sets. Biomarker analysis identified key genes associated with drug response, including CCR10 and PLEKHH2, WNT9B, ITPRID1, CHRNG, and DIRAS3. Ligand-based similarity screening against the COCONUT database followed by molecular docking and MD simulation identified three promising compounds (662142, 733302, and 883576) with improved binding affinity and conserved interactions with Topoisomerase-1. This integrative framework highlights the potential of combining machine learning, pharmacogenomics, and molecular modelling for biomarker discovery and drug prioritization in ovarian cancer.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"373-400"},"PeriodicalIF":2.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network toxicology and molecular docking elucidation of oligoasthenospermia induced by bisphenol analogues: a comparative study of BPA, BPS, BPF, and BPAF. 双酚类似物诱导少弱精子症的网络毒理学和分子对接研究：双酚a、BPS、BPF和BPAF的比较研究。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-04-01 Epub Date: 2026-04-20 DOI: 10.1080/1062936X.2026.2659333

H He, J Li, J Liu, J Zong, X Jiang, M Xiao, J Liu, Y Lyu

{"title":"Network toxicology and molecular docking elucidation of oligoasthenospermia induced by bisphenol analogues: a comparative study of BPA, BPS, BPF, and BPAF.","authors":"H He, J Li, J Liu, J Zong, X Jiang, M Xiao, J Liu, Y Lyu","doi":"10.1080/1062936X.2026.2659333","DOIUrl":"10.1080/1062936X.2026.2659333","url":null,"abstract":"Bisphenol A (BPA) substitutes are increasingly prevalent in consumer products, yet their potential to induce oligoasthenospermia (OAS) remains poorly understood. This study combined network toxicology, molecular docking, and 500-ns molecular dynamics (MD) simulations to systematically compare the toxicity mechanisms of BPA, BPS, BPF, and BPAF in the context of OAS. Intersection analysis identified 25 shared targets, with topological clustering highlighting ESR1, AR, CYP19A1, TNF, and IL6 as central hubs linking endocrine disruption with inflammatory pathways. Molecular docking revealed broad receptor engagement (-5.4 to -8.7 kcal/mol), with BPAF exhibiting the strongest affinities, particularly for ESR1 (-8.7 kcal/mol) and CYP19A1 (-8.4 kcal/mol). Molecular Dynamics (MD) simulations confirmed the dynamic stability of the ESR1-BPAF complex, demonstrating a compact fold (SASA ~124 nm2), minimal structural drift (ligand RMSD ~0.16 nm; protein RMSD ~0.20 nm), and persistent hydrogen bonding anchored by residues GLU353 and LEU428. These computational findings predict that BPA alternatives - especially BPAF - may significantly perturb steroidogenic and inflammatory axes to drive spermatogenic impairment. This study provides a predictive hazard prioritization framework that challenges the safety assumption of 'BPA-free' substitutes and warrants urgent experimental validation.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"311-325"},"PeriodicalIF":2.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drug synergy prediction using heterogeneous stacking ensemble learning. 基于异质堆叠集成学习的药物协同预测。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-04-01 Epub Date: 2026-05-22 DOI: 10.1080/1062936X.2026.2666425

P Rani, K Dutta, V Kumar

{"title":"Drug synergy prediction using heterogeneous stacking ensemble learning.","authors":"P Rani, K Dutta, V Kumar","doi":"10.1080/1062936X.2026.2666425","DOIUrl":"10.1080/1062936X.2026.2666425","url":null,"abstract":"Malignant diseases are considered the most prominent and widespread causes of death affecting populations globally. Synergistic drug combinations have shown beneficial therapeutic results in the treatment of malignant diseases. Although techniques such as clinical trials and high-throughput drug screening are commonly used to discover promising synergistic drug pairs, they are time-consuming and expensive. Over the past years, various AI-based drug synergy techniques including machine learning and deep learning have been utilized in finding synergistic drug combinations. Individually using these methods for synergy prediction has the disadvantages of overfitting and lack of interpretability. Combining different AI methods through ensemble learning provides better predictions by more closely representing the underlying distribution of data. This study utilized the heterogeneous stacking ensemble approach (HTeSyn) by aggregating four machine learning methods as base learners and one neural method as meta-learner.This multi-faceted approach helps in correcting classification results and provides more reliable synergy predictions, which is crucial for identifying effective drug combinations. For the bliss independence synergy task, HTeSyn outperforms the state-of-the-art synergy prediction method with an accuracy of 94%, RMSE of 12.5, and r2 of 0.8.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"37 4","pages":"327-347"},"PeriodicalIF":2.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QSPR models for water solubility of organic compounds using correlation intensity index and Las Vegas algorithm. 利用相关强度指数和Las Vegas算法建立有机化合物水溶性的QSPR模型。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-04-01 Epub Date: 2026-04-24 DOI: 10.1080/1062936X.2026.2659325

A P Toropova, A A Toropov, G Selvestrel, N Iovine, A Roncaglioni, E Benfenati

引用次数: 0

Mitigating silent failures in toxicity prediction: a conformalized heteroscedastic Bayesian framework. 减轻毒性预测中的沉默失败：一个符合规范的异方差贝叶斯框架。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-04-01 Epub Date: 2026-04-20 DOI: 10.1080/1062936X.2026.2658500

B Özlüer Başer

{"title":"Mitigating silent failures in toxicity prediction: a conformalized heteroscedastic Bayesian framework.","authors":"B Özlüer Başer","doi":"10.1080/1062936X.2026.2658500","DOIUrl":"10.1080/1062936X.2026.2658500","url":null,"abstract":"Accurate uncertainty quantification is a prerequisite for reliable toxicity assessments in drug discovery. Traditional QSAR models provide point estimates but fail to communicate prediction reliability, particularly for structurally complex compounds. We propose Conformalized Two-Stage Heteroscedastic BART (C2S-HBART), a novel framework addressing homoscedastic modelling assumptions. A first-stage BART model estimates the mean toxicity response; a second stage explicitly estimates local predictive variance from SMILES-derived structural descriptors. Split Conformal Prediction is then integrated to provide distribution-free validity guarantees. Evaluated on the Tox21 dataset, C2S-HBART achieves near-nominal coverage (0.952 vs. 0.880 for the uncalibrated baseline) and reduces the rate of silent failures - confident but incorrect predictions - from 12.02% to 1.8%. Compared to a standard conformalized baseline requiring wide intervals (Avg Width: 1.63), the heteroscedastic approach achieves equivalent safety with sharper predictions (Avg Width: 1.20), representing a 26% improvement in information efficiency. Variable importance analysis further reveals that molecular size and topological complexity are primary drivers of predictive uncertainty. C2S-HBART provides a statistically rigorous, transparent decision-support tool for preclinical screening, enabling toxicologists to prioritize safer compounds and flag structurally complex molecules for experimental validation.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"283-297"},"PeriodicalIF":2.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of acetylcholinesterase inhibition associated with Alzheimer's disease using hybrid descriptor and graph-based machine learning models. 使用混合描述符和基于图的机器学习模型预测与阿尔茨海默病相关的乙酰胆碱酯酶抑制

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-03-01 Epub Date: 2026-04-15 DOI: 10.1080/1062936X.2026.2647201

R U Laskar, B S Meena, A Roy, A Borah

{"title":"Prediction of acetylcholinesterase inhibition associated with Alzheimer's disease using hybrid descriptor and graph-based machine learning models.","authors":"R U Laskar, B S Meena, A Roy, A Borah","doi":"10.1080/1062936X.2026.2647201","DOIUrl":"10.1080/1062936X.2026.2647201","url":null,"abstract":"Accurate prediction of acetylcholinesterase (AChE) inhibitory activity is important in drug discovery and environmental toxicology because AChE inhibition represents a key mechanism underlying neurotoxicity associated with pharmaceuticals and environmental contaminants. In this study, machine learning approaches were used to develop predictive models for AChE inhibitory activity using experimentally measured bioactivity data for small molecules targeting human AChE. A curated dataset containing 5795 molecules was compiled from BindingDB to support reliable model development. Fifteen predictive models were evaluated, including twelve individual machine learning and deep learning models and three hybrid fusion models, using multiple molecular representations such as physicochemical descriptors derived from RDKit and PaDEL and graph-based molecular structures. Among the individual models, tree-based ensemble methods demonstrated strong baseline performance, indicating that physicochemical descriptors capture important chemical features associated with AChE inhibition. Graph neural networks, particularly Graph Isomorphism Network effectively learn structural patterns related to inhibitory activity. To integrate complementary molecular information, a late-fusion hybrid framework combining descriptor-based predictions and graph-based representations was implemented using leakage-safe stacking with a Ridge regression meta-learner. Across ten independent train-test splits, the best-performing hybrid model integrating PaDEL-based XGBoost and GIN achieved r2 = 0.7400 ± 0.0138, demonstrating improved and stable predictive performance over individual models.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"223-240"},"PeriodicalIF":2.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linear models, quantum molecular descriptors, and DSSC efficiency: an approach for evaluating potential new sensitizing dyes. 线性模型、量子分子描述符和DSSC效率：一种评估潜在的新增敏染料的方法。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-03-01 Epub Date: 2026-04-27 DOI: 10.1080/1062936X.2026.2647200

E F S Mattos, I F Vieira, G S Mendonça, N B da Costa

引用次数: 0

Optima: a GUI-based toolkit for developing and validating interpretable machine learning based supervised classification QSAR models. Optima：一个基于gui的工具包，用于开发和验证基于监督分类QSAR模型的可解释机器学习。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-03-01 Epub Date: 2026-04-16 DOI: 10.1080/1062936X.2026.2649822

I Dasgupta, R Roy, S Gayen

{"title":"Optima: a GUI-based toolkit for developing and validating interpretable machine learning based supervised classification QSAR models.","authors":"I Dasgupta, R Roy, S Gayen","doi":"10.1080/1062936X.2026.2649822","DOIUrl":"10.1080/1062936X.2026.2649822","url":null,"abstract":"The rapid advancement of ML algorithms and the increasing availability of large datasets have significantly transformed the landscape of predictive modelling in scientific research. In this context, we introduce Optima (OPTimized Interpretable Model Building & Analysis Toolkit), a user-friendly, Python-based GUI designed to simplify and accelerate the development of interpretable ML-based classification QSAR/QSPR/QSTR models. This toolkit offers an intuitive graphical user interface (GUI), enabling users with domain knowledge but limited coding experience to efficiently optimize, construct, and interpret various ML-based classification models. A most highlighting feature of this GUI is its fully customizable settings panel, allowing users to modify colour schemes, font sizes, axis labels, and plot dimensions to suit publication or presentation needs. By combining robust optimization with an explainable approach, the Optima toolkit improves classification QSAR model performance while ensuring transparency and reproducibility. This platform addresses a critical need by providing an intuitive GUI for rational dataset splitting, efficient feature selection, and the development of seven different ML-based classification QSAR models, covering the entire workflow from optimization to interpretation. The toolkit is presently available for Windows and can be downloaded from the provided link (https://github.com/Rahul-Roy-21/OPTIMA).","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"241-257"},"PeriodicalIF":2.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated chemometric modelling of histamine H₃ receptor for the identification of ligands from a natural product repository. 用于从天然产物库中识别配体的组胺H3受体的集成化学计量学建模。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-03-01 Epub Date: 2026-04-27 DOI: 10.1080/1062936X.2026.2651284

N S Jandali, L A Samuor, K Tawaha, M A Khanfar

{"title":"Integrated chemometric modelling of histamine H3 receptor for the identification of ligands from a natural product repository.","authors":"N S Jandali, L A Samuor, K Tawaha, M A Khanfar","doi":"10.1080/1062936X.2026.2651284","DOIUrl":"10.1080/1062936X.2026.2651284","url":null,"abstract":"The histamine H3 receptor (H3R) is a GPCR that regulates the release of multiple neurotransmitters and has emerged as an attractive target for CNS disorders. An integrated computational workflow was applied to identify H3R ligands from natural products by combining pharmacophore modelling, QSAR, docking and molecular dynamics (MD) algorithms. Structure-based and ligand-based pharmacophore models were developed and validated using ROC analysis against a set of active H3R inhibitors and DUD-E decoys. The resulting models (PHARM-1 and PHARM-2) achieved AUC values of 0.716 and 0.959, respectively, supporting their ability to discriminate active from inactive compounds. Both models were used to screen AnalytiCon Discovery database of natural products, yielding 23 hits that were subsequently prioritized by docking and QSAR model generated using genetic function approximation. Six candidates docked successfully and preserved the expected anchoring interaction with the conserved Asp114; four compounds showed favourable QSAR-predicted affinities (pKi ≈ 5.01-7.06) and high consensus docking scores. Finally, MD simulations of H3R complexed with the two promising hits, physostigmine and catharanthine, indicate stable complexes, and receptor fluctuations reflecting ligand-dependent conformational sampling. These results highlight several natural products as promising H3R ligand candidates and provide a practical multi-filter pipeline for prioritizing hits for experimental validation.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"37 3","pages":"259-282"},"PeriodicalIF":2.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QSAR-based design of antitubercular pyrrolidine carboxamides. 基于qsar的抗结核吡咯烷胺类药物设计。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-02-01 Epub Date: 2026-03-27 DOI: 10.1080/1062936X.2026.2637963

J G Hernández-Lima, I Mercado-Sánchez, R Chávez-Rocha, I Vargas-Rodríguez, M A Vázquez, A Bazán-Jiménez, M Del Refugio Cuevas-Flores, J Robles, M A García-Revilla

{"title":"QSAR-based design of antitubercular pyrrolidine carboxamides.","authors":"J G Hernández-Lima, I Mercado-Sánchez, R Chávez-Rocha, I Vargas-Rodríguez, M A Vázquez, A Bazán-Jiménez, M Del Refugio Cuevas-Flores, J Robles, M A García-Revilla","doi":"10.1080/1062936X.2026.2637963","DOIUrl":"10.1080/1062936X.2026.2637963","url":null,"abstract":"Tuberculosis affects 10 million people around the world, for this reason, the development of new drugs against M. tuberculosis (Mtb) is an urgent issue. Enoyl ACP reductase (InhA) is a relevant biological target because it's the role in the synthesis of mycolic acid, the building block of the cell wall. Here, we propose new anti-tuberculosis candidates based on a family of tested InhA pyrrolidine-carboxamides. The strategy starts building QSAR models using, for the first time, a set of topological-descriptors from QTAIM (LDMtrace, Ndeloc, Ntotal, and Max_eigenv), hydrogen bond energy (HB) of Docking simulations, and standard QSAR molecular-descriptors (MATS4m, VE1_Dzp, and RDF70m). The model with the best performance displays robust external and internal validation parameters. Finally, based on the most reliable QSAR model, a set of new molecules were proposed; six of them (p69, p71, p73, p75, p77, and p78) show an efficient calculated IC50 and adequate ADMET properties, suggesting an enhanced biological activity. Molecular Dynamics and MM/PBSA results show that p71 is the most reliable candidate for the inhibition of InhA, displaying a stable complex p71-InhA along 200 ns of simulation and a negative free binding energy, ∆Gbinding < 0.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"105-130"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147521928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0