Molecular Informatics最新文献_第3页

Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model. 利用QSAR/QSPR模型的直接逆分析改进分子设计。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-01-01 DOI: 10.1002/minf.202400227

Yuto Shino, Hiromasa Kaneko

引用次数: 0

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst. 利用威尔金森催化剂模型对乙烯加氢反应路径网络进行化学分析。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-01-01 Epub Date: 2024-08-09 DOI: 10.1002/minf.202400063

Philippe Gantzer, Ruben Staub, Yu Harabuchi, Satoshi Maeda, Alexandre Varnek

{"title":"Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.","authors":"Philippe Gantzer, Ruben Staub, Yu Harabuchi, Satoshi Maeda, Alexandre Varnek","doi":"10.1002/minf.202400063","DOIUrl":"10.1002/minf.202400063","url":null,"abstract":"Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography (\"chemography\") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400063"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910023","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

Structural Insight on the Selectivity of Calyx[4]Arene-Based Inhibitors of Mg^2+-Dependent Atp-Hydrolases. 基于Calyx[4]芳烃的Mg2+依赖性atp水解酶抑制剂选择性的结构分析。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-01-01 Epub Date: 2024-12-05 DOI: 10.1002/minf.202400200

Alexey Rayevsky, Maksym Platonov, Bulgakov Elijah, Dmytro Volochnyuk, Tetyana Veklich, Sergiy Cherenok, Roman Rodik, Vitaliy Kalchenko, Sergiy Kosterin

{"title":"Structural Insight on the Selectivity of Calyx[4]Arene-Based Inhibitors of Mg2+-Dependent Atp-Hydrolases.","authors":"Alexey Rayevsky, Maksym Platonov, Bulgakov Elijah, Dmytro Volochnyuk, Tetyana Veklich, Sergiy Cherenok, Roman Rodik, Vitaliy Kalchenko, Sergiy Kosterin","doi":"10.1002/minf.202400200","DOIUrl":"10.1002/minf.202400200","url":null,"abstract":"Located in plasma membranes, ATP hydrolases are involved in several dynamic transport processes, helping to control the movement of ions across cell membranes. ATP hydrolase acts as a transport protein, converting energy from ATP hydrolysis into transport molecules against their concentration gradients. In addition to energy metabolism and active transport, ATP hydrolase is essential for maintaining cellular homeostasis and cell function. This study focused on the domain architecture model of P-type ATPases, which participate in the reaction cycles of ATP hydrolysis carried out by membrane transport systems - Na+, K+-ATPase and Ca2+, Mg2+-ATPase. Targeted modulation of Na+, K+-ATPase and Ca2+, Mg2+-ATPase by unnatural drugs is of greatest interest due to the lack of known effectors. This new discovery presents a convenient model based on our recent experimental studies of the membrane structures and myocytes of the uterine smooth muscle, the myometrium. This current study strongly supports the fact that nanosized calix[4]arenes functionalised on the upper rings of the macrocycle with biologically active phosphonic acid fragments can serve as selective and potent inhibitors of cation-transporting electroenzymes. This is how we discovered that calix[4]arene of methylenebisphosphonic acid C-97 and calix[4]arene of bis-aminophosphonic acid C-107 selectively and effectively (I0.5 <100 nM) inhibit the activity of Mg2+, ATP-dependent electrogenic Na+ K+ plasma membrane pump. As drug discovery in the field of Mg2+-ATPase inhibitors is uncharted territory, basic research holds the key to explaining and predicting the mechanism of interaction and action of different classes of compounds. In light of the presented results, new calix[4]arene compounds can be used as potent inhibitors of Mg2+, ATP-dependent electrogenic ion pumps.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400200"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780628","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

ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction. ERL-ProLiGraph：用于结合亲和力预测的蛋白质配体图结构数据的增强表示学习。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-12-01 Epub Date: 2024-10-15 DOI: 10.1002/minf.202400044

Gloria Geine Paendong, Soualihou Ngnamsie Njimbouom, Candra Zonyfar, Jeong-Dong Kim

{"title":"ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.","authors":"Gloria Geine Paendong, Soualihou Ngnamsie Njimbouom, Candra Zonyfar, Jeong-Dong Kim","doi":"10.1002/minf.202400044","DOIUrl":"10.1002/minf.202400044","url":null,"abstract":"Predicting Protein-Ligand Binding Affinity (PLBA) is pivotal in drug development, as accurate estimations of PLBA expedite the identification of promising drug candidates for specific targets, thereby accelerating the drug discovery process. Despite substantial advancements in PLBA prediction, developing an efficient and more accurate method remains non-trivial. Unlike previous computer-aid PLBA studies which primarily using ligand SMILES and protein sequences represented as strings, this research introduces a Deep Learning-based method, the Enhanced Representation Learning on Protein-Ligand Graph Structured data for Binding Affinity Prediction (ERL-ProLiGraph). The unique aspect of this method is the use of graph representations for both proteins and ligands, intending to learn structural information continued from both to enhance the accuracy of PLBA predictions. In these graphs, nodes represent atomic structures, while edges depict chemical bonds and spatial relationship. The proposed model, leveraging deep-learning algorithms, effectively learns to correlate these graphical representations with binding affinities. This graph-based representations approach enhances the model's ability to capture the complex molecular interactions critical in PLBA. This work represents a promising advancement in computational techniques for protein-ligand binding prediction, offering a potential path toward more efficient and accurate predictions in drug development. Comparative analysis indicates that the proposed ERL-ProLiGraph outperforms previous models, showcasing notable efficacy and providing a more suitable approach for accurate PLBA predictions.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400044"},"PeriodicalIF":2.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470300","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

The freedom space - a new set of commercially available molecules for hit discovery. 自由空间--一组新的商业化分子，用于发现新药。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-12-01 Epub Date: 2024-08-22 DOI: 10.1002/minf.202400114

Mykola V Protopopov, Valentyna V Tararina, Fanny Bonachera, Igor M Dzyuba, Anna Kapeliukha, Serhii Hlotov, Oleksii Chuk, Gilles Marcou, Olga Klimchuk, Dragos Horvath, Erik Yeghyan, Olena Savych, Olga O Tarkhanova, Alexandre Varnek, Yurii S Moroz

{"title":"The freedom space - a new set of commercially available molecules for hit discovery.","authors":"Mykola V Protopopov, Valentyna V Tararina, Fanny Bonachera, Igor M Dzyuba, Anna Kapeliukha, Serhii Hlotov, Oleksii Chuk, Gilles Marcou, Olga Klimchuk, Dragos Horvath, Erik Yeghyan, Olena Savych, Olga O Tarkhanova, Alexandre Varnek, Yurii S Moroz","doi":"10.1002/minf.202400114","DOIUrl":"10.1002/minf.202400114","url":null,"abstract":"The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless \"hits\", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400114"},"PeriodicalIF":2.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142018020","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

Review of the 8^th autumn school in chemoinformatics. 第八届化学信息学秋季学校回顾。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-12-01 Epub Date: 2024-10-15 DOI: 10.1002/minf.202400037

Johann Gasteiger

引用次数: 0

DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes. DIGEP-Pred 2.0：用于预测药物诱导的细胞信号传导和基因表达变化的网络应用程序。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-12-01 Epub Date: 2024-07-09 DOI: 10.1002/minf.202400032

Sergey M Ivanov, Anastasia V Rudik, Alexey A Lagunin, Dmitry A Filimonov, Vladimir V Poroikov

{"title":"DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes.","authors":"Sergey M Ivanov, Anastasia V Rudik, Alexey A Lagunin, Dmitry A Filimonov, Vladimir V Poroikov","doi":"10.1002/minf.202400032","DOIUrl":"10.1002/minf.202400032","url":null,"abstract":"The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400032"},"PeriodicalIF":2.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141559261","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

Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines. 基于途径预测定制多草药的治疗效果和作用模式。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-11-01 Epub Date: 2024-10-15 DOI: 10.1002/minf.202400108

Akihiro Ezoe, Yuki Shimada, Ryusuke Sawada, Akihiro Douke, Tomokazu Shibata, Makoto Kadowaki, Yoshihiro Yamanishi

引用次数: 0

BIOMX-DB: A web application for the BIOFACQUIM natural product database. BIOMX-DB：BIOFACQUIM 天然产品数据库的网络应用程序。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-11-01 Epub Date: 2024-06-05 DOI: 10.1002/minf.202400060

Fernando Martínez-Urrutia, José L Medina-Franco

引用次数: 0

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules. 腐蚀科学的化学信息学：数据驱动的有机分子腐蚀抑制模型。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-11-01 Epub Date: 2024-10-15 DOI: 10.1002/minf.202400082

Igor Baskin, Yair Ein-Eli

{"title":"Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.","authors":"Igor Baskin, Yair Ein-Eli","doi":"10.1002/minf.202400082","DOIUrl":"10.1002/minf.202400082","url":null,"abstract":"This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400082"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470299","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