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Keywords = InChIkey

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26 pages, 3681 KiB  
Article
New Polymers In Silico Generation and Properties Prediction
by Andrey A. Knizhnik, Pavel V. Komarov, Boris V. Potapkin, Denis B. Shirabaykin, Alexander S. Sinitsa and Sergey V. Trepalin
Nanomanufacturing 2024, 4(1), 1-26; https://doi.org/10.3390/nanomanufacturing4010001 - 19 Dec 2023
Cited by 1 | Viewed by 1928
Abstract
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for [...] Read more.
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data. Full article
(This article belongs to the Special Issue Nanomanufacturing Empowered with Artificial Intelligence)
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14 pages, 1550 KiB  
Article
A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies’ Intercomparison
by Héctor Villalba, Maria Llambrich, Josep Gumà, Jesús Brezmes and Raquel Cumeras
Metabolites 2023, 13(12), 1167; https://doi.org/10.3390/metabo13121167 - 21 Nov 2023
Cited by 2 | Viewed by 2794
Abstract
Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step [...] Read more.
Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes’ retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies. Full article
(This article belongs to the Section Advances in Metabolomics)
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