化学最新文献_第7页

Python tools for structural tasks in chemistry. 用于化学结构任务的 Python 工具。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-05-14 DOI: 10.1007/s11030-024-10889-7

Fedor V Ryzhkov, Yuliya E Ryzhkova, Michail N Elinson

引用次数: 0

GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery. GraphkmerDTA：整合局部序列模式和拓扑信息进行药物-靶点结合亲和力预测及在多靶点抗阿尔茨海默病药物发现中的应用。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-01-10 DOI: 10.1007/s11030-024-11065-7

Zuolong Zhang, Gang Luo, Yixuan Ma, Zhaoqi Wu, Shuo Peng, Shengbo Chen, Yi Wu

{"title":"GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.","authors":"Zuolong Zhang, Gang Luo, Yixuan Ma, Zhaoqi Wu, Shuo Peng, Shengbo Chen, Yi Wu","doi":"10.1007/s11030-024-11065-7","DOIUrl":"10.1007/s11030-024-11065-7","url":null,"abstract":"Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3147-3164"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study. 基于机器学习的苯氧基亚胺催化剂活性预测及其构效关系研究。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-03-07 DOI: 10.1007/s11030-025-11147-0

Xiaoke Zhou, Sisi He, Min Xiao, Jing He, Yuan Wang, Yuanqin Zhu, Haixiang He

{"title":"Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study.","authors":"Xiaoke Zhou, Sisi He, Min Xiao, Jing He, Yuan Wang, Yuanqin Zhu, Haixiang He","doi":"10.1007/s11030-025-11147-0","DOIUrl":"10.1007/s11030-025-11147-0","url":null,"abstract":"This study systematically investigates the structure-activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R2 values of 0.998 (training set) and 0.859 (test set), with a cross-validated Q2 of 0.617. Feature importance analysis identified three composite descriptors-ODI_HOMO_1_Neg_Average GGI2, ALIEmax GATS8d, and Mol_Size_L-as critical contributors, collectively accounting for > 63% of the model's predictive power. Polynomial feature expansion effectively captured nonlinear interactions between descriptors, while SHAP and ICE analyses enhanced interpretability, revealing threshold effects and descriptor-specific trends. However, the model's generalizability may be constrained by the limited dataset size (30 samples) and reliance on density functional theory (DFT)-derived descriptors, necessitating experimental validation. Additionally, the study focused solely on ethylene polymerization at 40 °C; broader applicability to diverse catalytic systems or reaction conditions requires further validation. These findings provide a data-driven framework for catalyst design, though future work should integrate experimental validation and expand datasets to refine predictive robustness.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3411-3422"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity. 推进抗疟药物的发现：预测PfPK6抑制剂活性的集成机器学习模型。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-22 DOI: 10.1007/s11030-025-11203-9

Maryam Gholami, Mohammad Asadollahi-Baboli

{"title":"Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.","authors":"Maryam Gholami, Mohammad Asadollahi-Baboli","doi":"10.1007/s11030-025-11203-9","DOIUrl":"10.1007/s11030-025-11203-9","url":null,"abstract":"Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors, employing a range of machine learning techniques to develop ensemble regression and classification models. Molecular descriptors were refined using classification and regression trees (CART) to identify the most relevant features. Six machine learning algorithms (Random Forest (RF), Relevance Vector Machine (RVM), Support Vector Machine (SVM), Cubist, Artificial Neural Networks (ANN), and XGBoost) were utilized to construct regression models. The consensus model demonstrated superior predictive performance, achieving R2Test = 0.94, SETest = 0.20, Q2CV = 0.90, and SECV = 0.25, outperforming individual models. For classification tasks, five algorithms were evaluated and a majority voting approach yielded an accuracy of 91% and a sensitivity of 93%. The robustness of the models was confirmed through applicability domain analysis (96% coverage) and y-randomization tests, ensuring that the predictive outcomes were not due to chance correlations. This study highlights the effectiveness of ensemble machine learning approaches in predictive modeling and provides critical insights for the rational design of novel PfPK6 inhibitors.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3575-3586"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Targeting Poly (ADP-ribose) polymerase-1 (PARP-1) for DNA repair mechanism through QSAR-based virtual screening and MD simulation. 通过基于qsar的虚拟筛选和MD模拟，靶向聚（adp -核糖）聚合酶1 （PARP-1）的DNA修复机制。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-14 DOI: 10.1007/s11030-025-11184-9

Kun Cao, Ruonan Wang, Siyu Wu, Dong Ou, Ruixue Li, Lianhai Li, Xinguang Liu

{"title":"Targeting Poly (ADP-ribose) polymerase-1 (PARP-1) for DNA repair mechanism through QSAR-based virtual screening and MD simulation.","authors":"Kun Cao, Ruonan Wang, Siyu Wu, Dong Ou, Ruixue Li, Lianhai Li, Xinguang Liu","doi":"10.1007/s11030-025-11184-9","DOIUrl":"10.1007/s11030-025-11184-9","url":null,"abstract":"Poly (ADP-ribose) polymerase-1 (PARP-1) is a key enzyme in the base excision repair pathway, crucial for maintaining genomic stability by repairing DNA breaks. In cancers with mutations in DNA repair genes, such as BRCA1 and BRCA2, PARP-1 activity becomes essential for tumor cell survival, making it a promising target for therapeutic intervention. This study employs QSAR modeling, virtual screening, and molecular dynamics (MD) simulations to identify potential PARP-1 inhibitors. A dataset of inhibitors was analyzed using 12 molecular fingerprint descriptors to develop robust QSAR models, with the optimal model based on the CDK descriptor achieving R2 = 0.96, Q2_CV = 0.78, and Q2_Ext = 0.80. The model was applied to virtually screen three chemical libraries-ZINC, FDA, and NPA-identifying promising candidates for PARP-1 inhibition. Molecular docking revealed that compounds ZINC13132446, Z2037280227, and NPC193377 have strong binding affinity for the PARP-1 active site. MD simulations and MM-PBSA confirmed the stability of these complexes, with Z2037280227 and NPC193377 exhibiting the most stable interactions. These results underscore the potential of targeting PARP-1 as a therapeutic strategy for cancers with homologous recombination deficiencies, including prostate, breast, and ovarian cancer, particularly in patients with DNA repair deficiencies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3517-3535"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Unusual Cause of Hearing Loss: Incudal Osteoma. 听力损失的不寻常原因：内侧骨瘤。

IF 16.4 1区化学

Accounts of Chemical Research Pub Date : 2025-08-01 Epub Date: 2022-10-11 DOI: 10.1177/01455613221130887

Elif Gozgec, Hayri Ogul, Muhammed S Sakat

引用次数: 0

Measuring the Impact of a Delay in Care on Pediatric Otolaryngologic Surgery Completion. 衡量医疗延误对小儿耳鼻喉科手术完成率的影响。

IF 16.4 1区化学

Accounts of Chemical Research Pub Date : 2025-08-01 Epub Date: 2022-10-14 DOI: 10.1177/01455613221134428

Geethanjeli N Mahendran, Ching Siong Tey, Mary Frances Musso, Grace Shebha Anand, Jeffrey Larson, Mitesh Mehta, Lara Reichert, Kara Prickett, Nikhila Pinnapureddy Raol

{"title":"Measuring the Impact of a Delay in Care on Pediatric Otolaryngologic Surgery Completion.","authors":"Geethanjeli N Mahendran, Ching Siong Tey, Mary Frances Musso, Grace Shebha Anand, Jeffrey Larson, Mitesh Mehta, Lara Reichert, Kara Prickett, Nikhila Pinnapureddy Raol","doi":"10.1177/01455613221134428","DOIUrl":"10.1177/01455613221134428","url":null,"abstract":"Objective: To determine if postponement of elective pediatric otorhinolaryngology surgeries results in a change in overall healthcare utilization and if there is any commensurate impact on disease progression. Methods: We identified patients ≤18 years of age whose surgeries were postponed at the onset of the COVID-19 pandemic-related shutdown. We then tracked patients' rate of and patterns of rescheduling surgery. Surveys were also sent to caregivers to better characterize his/her decision regarding moving forward with his/her child's surgery during COVID-19. Results: A total of 1915 pediatric patients had elective surgeries canceled, of which 992 (51.8%) were rescheduled within 4 months. No difference in rates of rescheduling was identified based on race or ethnicity. Patients who were scheduled for tonsillectomies and/or adenoidectomies were 1.22 times more likely to reschedule compared to those patients with other planned procedures (CI: 1.02-1.46). A total of 95 caregivers at two hospitals completed surveys: 44 (47.4%) rescheduled their child's surgery. Most caregivers who rescheduled were concerned their child's disease could impact their future (n = 14, 32%). Conclusions: Just over half of patients who had pediatric otolaryngologic surgery canceled during a period of social distancing went on to have surgery within a 4-month timeframe. This reflects the dependence of pediatric otolaryngologic surgery on environmental exposures and may represent a potential target for prevention and management of some pediatric otolaryngology diseases.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"NP558-NP564"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33512690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survival comparison of different histological subtypes of oropharyngeal squamous cell carcinoma: A propensity-matched score analysis based on SEER database. 不同组织学亚型口咽鳞癌的生存期比较：基于 SEER 数据库的倾向匹配评分分析。

IF 16.4 1区化学

Accounts of Chemical Research Pub Date : 2025-08-01 Epub Date: 2022-11-01 DOI: 10.1177/01455613221136360

Tao Li, Yi Wang, Xianwang Xiang, Chuanjun Chen

{"title":"Survival comparison of different histological subtypes of oropharyngeal squamous cell carcinoma: A propensity-matched score analysis based on SEER database.","authors":"Tao Li, Yi Wang, Xianwang Xiang, Chuanjun Chen","doi":"10.1177/01455613221136360","DOIUrl":"10.1177/01455613221136360","url":null,"abstract":"ObjectiveThe purpose of this study was to analyze the difference of survival rates in paitents with oropharyngeal keratinizing squamous cell carcinoma (KSCC), nonkeratinizing squamous cell carcinoma (NKSCC), basaloid squamous cell carcinoma (BSCC), and papillary squamous cell carcinoma (PSCC).Materials and methodsPatients diagnosed with oropharyngeal squamous cell carcinoma between 2004 and 2015 were collected from the SEER database. Cox proportional hazards models and Kaplan-Meier curves were used for survival analysis. Propensity score matching (PSM) was performed to adjust for the effect of confounding variables. Due to the small sample size of PSCC, this study did not perform PSM between it and other subtypes.ResultsThe 5-year cancer-specific survival (CSS) rate of PSCC was higher than that of KSCC, NKSCC, and BSCC (0.627 vs. 0.812 vs. 0.789 vs. 0.875, P < 0.05); And the CSS rate of KSCC was lower than that of other subtypes both before and after PSM. In addition, the 5-year and 10-year CSS rates of BSCC were not different from NKSCC (P > 0.05), but not as good as NKSCC in the long term (P = 0.028). After PSM, the 5-year, 10-year, and long-term prognosis of BSCC were significantly worse than those of NKSCC (P < 0.001).ConclusionThe 5-year CSS of PSCC was better than the other three subtypes. The short-term prognosis of BSCC was not significantly different from NKSCC, but the long-term survival was lower than that of NKSCC, and the difference was more obvious after PSM. Meanwhile, the prognosis of KSCC was worst.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"NP518-NP528"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40447310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advertising and Front Matter. 广告和封面。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-08-01 Epub Date: 2025-07-24 DOI: 10.1177/00037028251359679

引用次数: 0

HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors. HDAC3_VS_assistant：化学信息学驱动的组蛋白去乙酰化酶3抑制剂的发现。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-12-23 DOI: 10.1007/s11030-024-11066-6

Oleg V Tinkov, Veniamin Y Grigorev

{"title":"HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.","authors":"Oleg V Tinkov, Veniamin Y Grigorev","doi":"10.1007/s11030-024-11066-6","DOIUrl":"10.1007/s11030-024-11066-6","url":null,"abstract":"Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD50, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity. The models employed molecular descriptors such as Morgan fingerprints, MACCS-166 keys, and Klekota-Roth, PubChem fingerprints integrated with machine learning algorithms including random forest, gradient boosting regressor, and support vector machine. The HDAC3 QSAR models achieved Q2test values of up to 0.76 and RMSE values as low as 0.58, while toxicity models attained Q2test values of 0.63 and RMSE values down to 0.41, with applicability domain (AD) coverage exceeding 68%. Internal validation by fivefold cross-validation (Q2cv = 0.70 for HDAC3 and 0.60 for toxicity) and y-randomization confirmed model reliability. Shapley additive explanation (SHAP) was also used to explain the influence of modeling features on model prediction results. The most predictive QSAR models are integrated into the developed HDAC3_VS_assistant application, which is freely available at https://hdac3-vs-assistant-v2.streamlit.app/ . Virtual screening conducted using the HDAC3_VS_assistant web application allowed us to reveal a number of potential inhibitors, and the nature of their bonds with the active HDAC3 site was additionally investigated by molecular docking.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3165-3187"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0