Molecular Informatics最新文献_第9页

CIPSI: An open chemical intellectual property service for medicinal chemists. CIPSI:为药物化学家提供开放的化学知识产权服务。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-12-12 DOI: 10.1002/minf.202300221

Maria Martinez-Sevillano, Maria J Falaguera, Jordi Mestres

引用次数: 0

GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit. GUIDEMOL：基于RDKit的分子描述符的Python图形用户界面。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-11-20 DOI: 10.1002/minf.202300190

Joao Aires-de-Sousa

引用次数: 0

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. 使用扩展生成建模（HIDDEN-GEM）丰富的对接发现命中率：一种用于加速超大型化学库虚拟筛选的新计算工作流。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-12-19 DOI: 10.1002/minf.202300207

Konstantin I Popov, James Wellnitz, Travis Maxfield, Alexander Tropsha

{"title":"HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.","authors":"Konstantin I Popov, James Wellnitz, Travis Maxfield, Alexander Tropsha","doi":"10.1002/minf.202300207","DOIUrl":"10.1002/minf.202300207","url":null,"abstract":"Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300207"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41139125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors. 以分枝杆菌的基本复制酶 DnaG primase 为靶标，发现米托蒽醌和伐普瑞泰是新型的分枝杆菌生长抑制剂。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-20 DOI: 10.1002/minf.202300284

Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover

{"title":"Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.","authors":"Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover","doi":"10.1002/minf.202300284","DOIUrl":"https://doi.org/10.1002/minf.202300284","url":null,"abstract":"Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex-vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"55 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-14 DOI: 10.1002/minf.202300206

Jaime Pérez-Villanueva, Karen Rodríguez-Villar, Francisco Cortés-Benítez, Juan Francisco Palacios-Espinosa

引用次数: 0

Use of tree-based machine learning methods to screen affinitive peptides based on docking data. 使用基于树的机器学习方法筛选基于对接数据的亲和肽。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300143

Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang

{"title":"Use of tree-based machine learning methods to screen affinitive peptides based on docking data.","authors":"Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang","doi":"10.1002/minf.202300143","DOIUrl":"10.1002/minf.202300143","url":null,"abstract":"Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300143"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10212086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the duration of action of β2-adrenergic receptor agonists: Ligand and structure-based approaches. 预测β2-肾上腺素能受体激动剂的作用持续时间：基于配体和结构的方法。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300141

Luca Chiesa, Emilie Sick, Esther Kellenberger

引用次数: 0

An in silico investigation of Kv2.1 potassium channel: Model building and inhibitors binding sites analysis. Kv2.1钾通道的计算机研究：模型构建和抑制剂结合位点分析。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1002/minf.202300072

Xiaoyu Wang, Xinyuan Zhang, Jie Zhou, Weiping Wang, Xiaoliang Wang, Bailing Xu

引用次数: 0

Classification of tastants: A deep learning based approach. 味觉分类：一种基于深度学习的方法。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300146

Prantar Dutta, Deepak Jain, Rakesh Gupta, Beena Rai

{"title":"Classification of tastants: A deep learning based approach.","authors":"Prantar Dutta, Deepak Jain, Rakesh Gupta, Beena Rai","doi":"10.1002/minf.202300146","DOIUrl":"10.1002/minf.202300146","url":null,"abstract":"Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300146"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

AliNA - a deep learning program for RNA secondary structure prediction. AliNA-一个用于RNA二级结构预测的深度学习程序。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-02 DOI: 10.1002/minf.202300113

Shamsudin S Nasaev, Artem R Mukanov, Ivan I Kuznetsov, Alexander V Veselovsky

引用次数: 0