Journal of Chemical Information and Modeling 最新文献_第4页

iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-04 DOI: 10.1021/acs.jcim.4c02192

Sayyed Jalil Mahdizadeh, Leif A Eriksson

{"title":"iScore: A ML-Based Scoring Function for De Novo Drug Discovery.","authors":"Sayyed Jalil Mahdizadeh, Leif A Eriksson","doi":"10.1021/acs.jcim.4c02192","DOIUrl":"10.1021/acs.jcim.4c02192","url":null,"abstract":"In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable speed and precision. Uniquely, iScore circumvents the conventional reliance on explicit knowledge of protein-ligand interactions and a full picture of atomic contacts, instead leveraging a set of ligand and binding pocket descriptors to directly evaluate binding affinity. This approach enables skipping the inefficient and slow conformational sampling stage, thereby enabling the rapid screening of ultrahuge molecular libraries, a crucial advancement given the practically infinite dimensions of chemical space. iScore was rigorously trained and validated using the PDBbind 2020 refined set, CASF 2016, CSAR NRC-HiQ Set1/2, DUD-E, and target fishing data sets, employing three distinct ML methodologies: Deep neural network (iScore-DNN), random forest (iScore-RF), and eXtreme gradient boosting (iScore-XGB). A hybrid model, iScore-Hybrid, was subsequently developed to incorporate the strengths of these individual base learners. The hybrid model demonstrated a Pearson correlation coefficient (R) of 0.78 and a root-mean-square error (RMSE) of 1.23 in cross-validation, outperforming the individual base learners and establishing new benchmarks for scoring power (R = 0.814, RMSE = 1.34), ranking power (ρ = 0.705), and screening power (success rate at top 10% = 73.7%). Moreover, iScore-Hybrid demonstrated great performance in the target fishing benchmarking study.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2759-2772"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-02-27 DOI: 10.1021/acs.jcim.4c02401

Andrés Halabi Diaz, Mario Duque-Noreña, Elizabeth Rincón, Eduardo Chamorro

{"title":"Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.","authors":"Andrés Halabi Diaz, Mario Duque-Noreña, Elizabeth Rincón, Eduardo Chamorro","doi":"10.1021/acs.jcim.4c02401","DOIUrl":"10.1021/acs.jcim.4c02401","url":null,"abstract":"Nitroaromatic compounds (NAs) are widely used in industrial applications but pose significant genotoxic risks, necessitating accurate mutagenicity prediction for chemical safety assessments. This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. Following OECD QSAR guidelines, feature selection and model development were performed using decision-tree-based algorithms (Random Tree, JCHAID*, SPAARC) and multilayer perceptrons (MLPs). These models exhibited high predictive accuracy (internal: >80%, κ = 0.21-0.37; external: ∼90%, κ = 0.41-0.62) with strong interpretability. The study also explores the role of metabolic activation and aqueous-phase descriptors, evaluating a novel electronic analog to LogP (LogQP) to assess hydrophobicity-mutagenicity relationships. Results demonstrate that aqueous-phase electronic properties and electrophilicity descriptors outperform vacuum-based methods in mutagenicity prediction. The combination of CDFT descriptors with shallow ML models proves to be a robust, interpretable, and accessible framework for predictive toxicology. This approach enhances chemical risk assessment and bridges computational chemistry with toxicology for regulatory applications.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2950-2960"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522070","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

MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-11 DOI: 10.1021/acs.jcim.5c00043

Yu Li, Lin-Xuan Hou, Hai-Cheng Yi, Zhu-Hong You, Shi-Hong Chen, Jia Zheng, Yang Yuan, Cheng-Gang Mi

{"title":"MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.","authors":"Yu Li, Lin-Xuan Hou, Hai-Cheng Yi, Zhu-Hong You, Shi-Hong Chen, Jia Zheng, Yang Yuan, Cheng-Gang Mi","doi":"10.1021/acs.jcim.5c00043","DOIUrl":"10.1021/acs.jcim.5c00043","url":null,"abstract":"Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In this study, we propose MOLGAECL, a novel approach based on graph autoencoder pretraining and molecular graph contrastive learning. Initially, a large number of unlabeled molecular graphs are pretrained using a graph autoencoder, where graph contrastive learning is applied for more accurate representation of the drugs. Subsequently, a full-parameter fine-tuning is performed on different data sets to adapt the model for drug interaction-related prediction tasks. To assess the effectiveness of MOLGAECL, comparison experiments with state-of-the-art methods, fine-tuning comparison experiments, and parameter sensitivity analysis are conducted. Extensive experimental results demonstrate the superior performance of MOLGAECL. Specifically, MOLGAECL achieves an average increase of 6.13% in accuracy, 6.14% in AUROC, and 8.16% in AUPRC across all data sets.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"3104-3116"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603041","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

ASVirus: A Comprehensive Knowledgebase for the Viral Alternative Splicing.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-10 DOI: 10.1021/acs.jcim.4c02214

Yu-Hong Liu, Hong-Quan Xu, Si-Si Zhu, Yan-Feng Hong, Xiu-Wen Li, Hong-Xiu Li, Jun-Peng Xiong, Huan Xiao, Jin-Hui Bu, Feng Zhu, Lin Tao

{"title":"ASVirus: A Comprehensive Knowledgebase for the Viral Alternative Splicing.","authors":"Yu-Hong Liu, Hong-Quan Xu, Si-Si Zhu, Yan-Feng Hong, Xiu-Wen Li, Hong-Xiu Li, Jun-Peng Xiong, Huan Xiao, Jin-Hui Bu, Feng Zhu, Lin Tao","doi":"10.1021/acs.jcim.4c02214","DOIUrl":"10.1021/acs.jcim.4c02214","url":null,"abstract":"Viruses are significant human pathogens responsible for pandemic outbreaks and seasonal epidemics. Viral infectious diseases impose a devastating global burden and have a profound impact on public health systems. During viral infections, alternative splicing (AS) plays a crucial role in regulating immune responses, altering the host's cellular environment, expanding viral genetic material, and facilitating viral replication. As research on AS in viral infections expands, it is crucial to consolidate data on virus-related splicing changes to improve our understanding of these viruses and associated diseases. To address this need, we created ASVirus (https://bddg.hznu.edu.cn/asvirus/), a comprehensive database of virus-associated AS events and their regulatory factors. ASVirus uniquely combines high-confidence, experimentally validated splicing data and investigates upstream regulatory mechanisms through a gene-splicing factor interaction network. Its user-friendly web interface offers detailed information into AS events from various viral families and the resulting mis-splicing in host genes, aiding the exploration of novel viral infection mechanisms and the identification of critical therapeutic targets for viral diseases.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2722-2729"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595798","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

Fluor-Predictor: An Interpretable Tool for Multiproperty Prediction and Retrieval of Fluorescent Dyes.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-12 DOI: 10.1021/acs.jcim.5c00127

Wenxiang Song, Le Xiong, Xinmin Li, Yuyang Zhang, Binya Wang, Guixia Liu, Weihua Li, Youjun Yang, Yun Tang

{"title":"Fluor-Predictor: An Interpretable Tool for Multiproperty Prediction and Retrieval of Fluorescent Dyes.","authors":"Wenxiang Song, Le Xiong, Xinmin Li, Yuyang Zhang, Binya Wang, Guixia Liu, Weihua Li, Youjun Yang, Yun Tang","doi":"10.1021/acs.jcim.5c00127","DOIUrl":"10.1021/acs.jcim.5c00127","url":null,"abstract":"With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is a lack of tools for comprehensive data integration and convenient data retrieval. Moreover, existing prediction models mainly focus on a single property of fluorescent dyes and fail to account for the diverse fluorophores and solutions in a systematic manner. To address this, we proposed Fluor-predictor, a multitask prediction model for fluorophores. This study integrates multiple dye databases and develops an interpretable graph neural network-based multitask regression model to predict four key optical properties of fluorescent dyes. We thoroughly examined the impact of factors such as data quality and the number of solvents on model performance. By leveraging atomic weight contributions, the model not only predicts these properties but also provides insights to guide structural modifications. In addition, we compiled and built a comprehensive database containing 36,756 records of fluorescence properties. To address the limitations of existing models in accurate prediction of Xanthene and Cyanine dyes, we then compiled 1148 Xanthene dye records and 1496 Cyanine dye records from the literature, comparing direct training with transfer learning approaches. The model achieved mean absolute errors (MAE) of 11.70 nm, 15.37 nm, 0.096, and 0.091 for predicting absorption wavelength (λabs), emission wavelength (λem), quantum yield (Φ) and molar extinction coefficient (Log(ε)), respectively. We integrated this work into a tool, Fluor-predictor, which supports comprehensive retrieval methods and multiproperty prediction. Fluor-predictor will facilitate data retrieval, prescreening, and structural modification of dyes.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2854-2867"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603037","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

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 DOI: 10.1021/acs.jcim.4c02291

Liang Zhang, Kuan Luo, Ziyi Zhou, Yuanxi Yu, Fan Jiang, Banghao Wu, Mingchen Li, Liang Hong

{"title":"A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.","authors":"Liang Zhang, Kuan Luo, Ziyi Zhou, Yuanxi Yu, Fan Jiang, Banghao Wu, Mingchen Li, Liang Hong","doi":"10.1021/acs.jcim.4c02291","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02291","url":null,"abstract":"The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pHopt) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining and de novo design has produced a large number of new enzymes, making it impractical to measure their pHopt in the wet laboratory. Consequently, in-silico computational approaches such as machine learning and deep learning models, which offer pH prediction at minimal cost, have attracted considerable interest. This work presents Venus-DREAM, an enzyme pHopt prediction model based on the kNN algorithm and few-shot learning, which achieves state-of-the-art accuracy in pHopt prediction. Venus-DREAM regards the pHopt prediction of an enzyme as a few-shot learning task: learning from the k-closest labeled enzymes to predict the pHopt of the target enzyme. The value of k is determined by the optimal k-value of the kNN regression algorithm. And the distance between two enzymes is defined as the cosine similarity of their mean-pooled embeddings obtained from protein language models (PLMs). The few-shot learner is based on the Reptile algorithm, which first adapts to the k-nearest labeled enzymes to create a specialized model for the target enzyme and then predicts its pHopt. This efficient method enables high-throughput virtual exploration of protein space, facilitating the identification of sequences with the desired pHopt ranges in a high-throughput manner. Moreover, our method can be easily adapted in other protein function prediction tasks.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699094","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

Strategic Design of Fluorescent Perylene-Modified Nucleic Acid Monomers: Position-, Phosphorylation-, and Linker-Dependent Control of Electron Transfer.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-03 DOI: 10.1021/acs.jcim.4c02223

Solomon Y Effah, Mark A Hix, Alice R Walker

{"title":"Strategic Design of Fluorescent Perylene-Modified Nucleic Acid Monomers: Position-, Phosphorylation-, and Linker-Dependent Control of Electron Transfer.","authors":"Solomon Y Effah, Mark A Hix, Alice R Walker","doi":"10.1021/acs.jcim.4c02223","DOIUrl":"10.1021/acs.jcim.4c02223","url":null,"abstract":"Synthetic fluorescent nucleotides (SFNs) have a wide variety of applications in biochemical tracking, imaging, and diagnostic assays. There are many SFNs in active development to enhance their fluorescence wavelengths, environmental sensitivity, photostability, and photochemical/photoswitchable properties. However, there are few systematic theoretical studies of their fluorescence properties. In this work, we apply excited-state QM/MM dynamics with TDDFT to nucleic acids tagged with perylene, which is particularly photostable, fluorescent, and bright (fluorescence quantum yield = 0.94) in isolation. We demonstrate that the overall structure, phosphorylation state, and linker type control electron transfer and fluorescence properties. A critical 90° dihedral angle between the perylene tag and nucleobase drives rapid quenching through charge transfer pathways, with positions 7 and 8 on guanine showing higher quenching propensity than position 2. Potential energy profiles reveal that the accessibility of the 90° base-tag geometries is critical for charge transfer, with the attachment position controlling this accessibility through steric and electronic effects. The presence of a phosphate group modulates this process by stabilizing excited states and reducing charge transfer rates. Additionally, ethynylene linkers maintain fluorescence through reduced angular dependence. The directionality of electron transfer stems primarily from the nucleic acid type, with guanine showing bidirectional transfer depending on the initial geometry, while adenine remains stable without significant charge transfer. These findings provide structural design principles for improved synthetic fluorescent nucleotides with tailored charge transfer characteristics.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2940-2949"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539499","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

Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-12 DOI: 10.1021/acs.jcim.4c02424

Yanglan Gan, Shengnan Li, Guangwei Xu, Cairong Yan, Guobing Zou

{"title":"Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.","authors":"Yanglan Gan, Shengnan Li, Guangwei Xu, Cairong Yan, Guobing Zou","doi":"10.1021/acs.jcim.4c02424","DOIUrl":"10.1021/acs.jcim.4c02424","url":null,"abstract":"The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced to predict possible drug-disease relationships. Nevertheless, current graph-based methods tend to model drug-disease interaction relationships without considering the semantic influence of node-specific side information on graphs. These approaches also suffer from the noise and sparsity inherent in the data. To address these limitations, we propose MDGCN, a novel drug repositioning method that incorporates multidependency graph convolutional networks and contrastive learning. Based on drug and disease similarity matrices and the drug-disease relationships matrix, this approach constructs multidependency graphs. It subsequently employs graph convolutional networks to spread side information between various graphs in each layer. Meanwhile, the weak supervision of drug-disease connections is effectively addressed by introducing cross-view and cross-layer contrastive learning to align node embedding across various views. Extensive experiments show that MDGCN performs better in drug-disease association prediction than seven advanced methods, offering strong support for investigating novel therapeutic indications for medications of interest.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"3090-3103"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603044","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

FDPSM: Feature-Driven Prediction Modeling of Pathogenic Synonymous Mutations.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-13 DOI: 10.1021/acs.jcim.4c02139

Fangfang Jin, Na Cheng, Lihua Wang, Bin Ye, Junfeng Xia

{"title":"FDPSM: Feature-Driven Prediction Modeling of Pathogenic Synonymous Mutations.","authors":"Fangfang Jin, Na Cheng, Lihua Wang, Bin Ye, Junfeng Xia","doi":"10.1021/acs.jcim.4c02139","DOIUrl":"10.1021/acs.jcim.4c02139","url":null,"abstract":"Synonymous mutations, once considered to be biologically neutral, are now recognized to affect protein expression and function by altering the RNA splicing, stability, or translation efficiency. These effects can contribute to disease, making the prediction of the pathogenicity a crucial task. Computational methods have been developed to analyze the sequence features and biological functions of synonymous mutations, but existing methods face limitations, including scarcity of labeled data, reliance on other prediction tools, and insufficient representation of feature interrelationships. Here, we present FDPSM, a novel prediction method specifically designed to predict pathogenic synonymous mutations. FDPSM was trained on a robust data set of 4251 positive and negative training samples to enhance predictive accuracy. The method leveraged a comprehensive set of features, including genomic context, conservation, splicing effects, functional effects, and epigenomics, without relying on prediction scores from other mutation pathogenicity tools. Recognizing that original features alone may not fully capture the distinctions between pathogenic and benign synonymous mutations, we enhanced the feature set by extracting effective information from the interactions and distribution of these features. The experimental results showed that FDPSM significantly outperformed existing methods in predicting the pathogenicity of synonymous mutations, offering a more accurate and reliable tool for this important task. FDPSM is available at https://github.com/xialab-ahu/FDPSM.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"3064-3076"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622861","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

Fourier Spectral Deconvolution to Describe Behaviors of pH-Dependent Monomeric and Self-Associated Anthocyanin Species.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-24 Epub Date: 2025-03-03 DOI: 10.1021/acs.jcim.4c02300

Rachael A Tindal, David W Jeffery, Richard A Muhlack

{"title":"Fourier Spectral Deconvolution to Describe Behaviors of pH-Dependent Monomeric and Self-Associated Anthocyanin Species.","authors":"Rachael A Tindal, David W Jeffery, Richard A Muhlack","doi":"10.1021/acs.jcim.4c02300","DOIUrl":"10.1021/acs.jcim.4c02300","url":null,"abstract":"Anthocyanins are pigmented polyphenolic compounds that influence the color, stability, and quality of numerous plant organs (e.g., fruit, flowers) and their derived products (e.g., natural dyes, red wine). Existing within a complex multistate system, anthocyanins can be simultaneously present as pH-dependent red, purple, or blue species that are either in a monomeric (chemically unstable) or in a self-associated (temporally stable) form. However, limitations of current analytical techniques (e.g., HPLC with a UV-vis detector) may cause experimental data to omit or misrepresent important color and stability characteristics afforded by all anthocyanin species within the natural matrix. In response, a computational spectral deconvolution method is demonstrated that increases the fidelity of spectral data collected for anthocyanins, thereby representing all existing monomeric and self-associated anthocyanin species within solution for the first time. Case studies for the developed deconvolution model are presented, based on experimental data obtained via HPLC-DAD analysis for malvidin-3-O-β-d-glucopyranoside (M3G) in red wines that were sampled throughout fermentation. Fourier spectral deconvolution methods were used to transform experimental spectra for pigmented anthocyanin monomers into systems that represent spectral behaviors of all pigmented monomeric and self-associated anthocyanin species in solution. The developed computational model was found to significantly increase the level of signal feature extraction for the spectral data of anthocyanins, providing key information on color expression and stability characteristics that would be otherwise unattainable with traditional approaches using HPLC with UV-vis detection. The current work increases understanding and control over key attributes of anthocyanins and has broad potential applications for the analysis and commercialization of anthocyanin-containing products.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2834-2844"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11939003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0