Journal of Cheminformatics最新文献

{"title":"Structure-based machine learning screening identifies natural product candidates as potential geroprotectors.","authors":"Jose Alberto Santiago-de-la-Cruz,Nadia Alejandra Rivero-Segura,Juan Carlos Gomez-Verjan","doi":"10.1186/s13321-025-01058-5","DOIUrl":"https://doi.org/10.1186/s13321-025-01058-5","url":null,"abstract":"Age-related diseases and syndromes result in poor quality of life and adverse outcomes, representing a challenge to healthcare systems worldwide. Several pharmacological interventions have been proposed to target the aging process to slow its adverse effects. The so-called geroprotectors have been proposed as novel molecules that could maintain the organism's homeostasis, targeting specific aspects linked to the hallmarks of aging and delaying the adverse outcomes associated with age. On the other hand, machine learning (ML) is revolutionising drug design by making the process faster, cheaper, and more efficient.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"10 1","pages":"106"},"PeriodicalIF":8.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640267","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}

{"title":"SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins","authors":"Xiaobo Lin, Zhaoqian Su, Yunchao Lance Liu, Jingxian Liu, Xiaohan Kuang, Peter T. Cummings, Jesse Spencer-Smith, Jens Meiler","doi":"10.1186/s13321-025-01038-9","DOIUrl":"https://doi.org/10.1186/s13321-025-01038-9","url":null,"abstract":"Metal ions, as abundant and vital cofactors in numerous proteins, are crucial for enzymatic activities and protein interactions. Given their pivotal role and catalytic efficiency, accurately and efficiently identifying metal-binding sites is fundamental to elucidating their biological functions and has significant implications for protein engineering and drug discovery. To address this challenge, we present SuperMetal, a generative AI framework that leverages a score-based diffusion model coupled with a confidence model to predict metal-binding sites in proteins with high precision and efficiency. Using zinc ions as an example, SuperMetal outperforms existing state-of-the-art models, achieving a precision of 94 % and coverage of 90 %, with zinc ions localization within 0.52 ± 0.55 Å of experimentally determined positions, thus marking a substantial advance in metal-binding site prediction. Furthermore, SuperMetal demonstrates rapid prediction capabilities (under 10 s for proteins with $$sim$$ 2000 residues) and remains minimally affected by increases in protein size. Notably, SuperMetal does not require prior knowledge of the number of metal ions—unlike AlphaFold 3, which depends on this information. Additionally, SuperMetal can be readily adapted to other metal ions or repurposed as a probe framework to identify other types of binding sites, such as protein-binding pockets. Scientific contribution SuperMetal introduces a diffusion-based, SE(3)-equivariant generative model that places metal ions in proteins with 94 % precision, 90 % coverage, and sub-ångström (0.52 Å) accuracy in under 10 s, surpassing current methods and accelerating metal-aware protein engineering and drug discovery.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"10 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640396","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}

{"title":"Milestones in cheminformatics","authors":"Karina Martinez-Mayorga, José L. Medina-Franco","doi":"10.1186/s13321-025-01054-9","DOIUrl":"https://doi.org/10.1186/s13321-025-01054-9","url":null,"abstract":"<p>The field of cheminformatics has undergone significant transformation since its inception, evolving from a niche discipline to a cornerstone of modern medicinal chemistry, pharmaceutical research, and several other areas of chemistry [1,2,3]. To celebrate the 15th anniversary of the <i>Journal of Cheminformatics</i>, we present the special collection <i>Milestones in Cheminformatics</i>, https://www.biomedcentral.com/collections/MICHE, showcasing how cheminformatics has grown into a key discipline underpinning chemical research, innovation, and applications. The collection is intended to serve not only as a retrospective view but also as a platform to envision the future directions of cheminformatics.</p><p>This special issue brings together perspectives from renowned scholars and practitioners who highlight transformative developments across various domains of cheminformatics. Bajorath offers a global perspective on the trajectory of the field, setting the stage for future integration and growth. Willett revisits the early efforts in chemical database search. Reymond highlights the conceptual and practical implications of chemical space as a unifying theme, offering insights into its role in visualizing diversity and guiding discovery. Tropsha and colleagues present a critical analysis of current paradigms for assessing the accuracy of QSAR models, arguing for more nuanced and task-specific validation strategies. Steinbeck traces the journey from closed systems to collaborative innovation, while Williams and Richard propose three pillars for ensuring public access and data integrity in chemical databases. Bienstock discusses the impact and potential of AI/ML methods in designing new chemical entities, underscoring their growing role in predictive modeling and virtual screening. Rather than a closing section, Varnek et al. touches an important aspect of the future by exploring achievements and challenges in higher education, emphasizing the need for structured cheminformatics curricula and interdisciplinary competencies.</p><p>Contributions in this collection illustrate the multidimensional character of cheminformatics—from its computational and theoretical foundations to its educational, ethical, and infrastructural components. The collection highlights both the progress achieved and the challenges that remain, such as harmonizing data standards, ensuring reproducibility, and fostering inclusive access to tools and knowledge [3], while also intersecting with disciplines like bioinformatics, materials science, and systems biology [4]. Transparency, collaboration, and interdisciplinary interactions are poised to become key drivers of future developments in the field. The rise of explainable artificial intelligence and sustainable data practices are likely to define the next era of cheminformatics.</p><p>The field of cheminformatics has been sculptured by many scientists—those who contributed to this special collection, those who were unable to ","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"7 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629919","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}

{"title":"Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields","authors":"Shih-Cheng Li, Pei-Hua Wang, Jheng-Wei Su, Wei-Yin Chiang, Tzu-Lan Yeh, Alex Zhavoronkov, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen","doi":"10.1186/s13321-025-01043-y","DOIUrl":"https://doi.org/10.1186/s13321-025-01043-y","url":null,"abstract":"Finding optimal reaction conditions is crucial for chemical synthesis in the pharmaceutical and chemical industries. However, due to the vast chemical space, conducting experiments for all the possible combinations is impractical. Thus, quantitative structure–activity relationship (QSAR) models have been widely used to predict product yields, but evaluating all combinations is still computationally intensive. In this work, we demonstrate the use of Digital Annealer Unit (DAU) can tackle these large-scale optimization problems more efficiently. Two types of models are developed and tested on high-throughput experimentation (HTE) and Reaxys datasets. Our results suggest that the performance of models is comparable to classical machine learning (ML) methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. In active learning and autonomous reaction condition design, our model shows improvement for reaction yield prediction by incorporating new data, meaning that it can potentially be used in iterative processes. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. This study demonstrates the application of DAUs to efficiently optimize chemical reaction conditions, leveraging quadratic unconstrained binary optimization (QUBO) models for accurate yield predictions. The QUBO-based approach exhibits comparable performance to classical machine learning methods while achieving inference times in seconds, significantly accelerating the screening of billions of reaction conditions. By integrating active learning and DAU technology, this research establishes a novel framework for reaction condition optimization, enabling innovative advancements in chemical synthesis.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"10 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629918","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}

{"title":"A transformer based generative chemical language AI model for structural elucidation of organic compounds","authors":"Xiaofeng Tan","doi":"10.1186/s13321-025-01016-1","DOIUrl":"https://doi.org/10.1186/s13321-025-01016-1","url":null,"abstract":"For over half a century, computer-aided structural elucidation systems (CASE) for organic compounds have relied on complex expert systems with explicitly programmed algorithms. These systems are often computationally inefficient for complex compounds due to the vast chemical structural space that must be explored and filtered. In this study, we present a proof-of-concept transformer based generative chemical language artificial intelligence (AI) model, an innovative end-to-end architecture designed to replace the logic and workflow of the classic CASE framework for ultra-fast and accurate spectroscopic-based structural elucidation. Our model employs an encoder-decoder architecture and self-attention mechanisms, similar to those in large language models, to directly generate the most probable chemical structures that match the input spectroscopic data. Trained on ~ 102 k IR, UV, and 1H NMR spectra, it performs structural elucidation of molecules with up to 29 atoms in just a few seconds on a modern CPU, achieving a top-15 accuracy of 83%. This approach demonstrates the potential of transformer based generative AI to accelerate traditional scientific problem-solving processes. The model's ability to iterate quickly based on new data highlights its potential for rapid advancements in structural elucidation. This study introduces a transformer-based generative AI model as a novel approach to structural elucidation for organic compounds, replacing traditional CASE systems with an end-to-end encoder-decoder architecture. This work demonstrates the potential of transformer models to revolutionize CASE by significantly accelerating the elucidation process and enabling rapid iterations with new data.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"91 18 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611450","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}

{"title":"GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target","authors":"Ana B. Caniceiro, Ana M. B. Amorim, Nícia Rosário-Ferreira, Irina S. Moreira","doi":"10.1186/s13321-025-01050-z","DOIUrl":"https://doi.org/10.1186/s13321-025-01050-z","url":null,"abstract":"G Protein-Coupled Receptors (GPCRs) are vital players in cellular signalling and key targets for drug discovery, especially within the GPCR-A17 subfamily, which is linked to various diseases. To address the growing need for effective treatments, the GPCR-A17 Modulator, Agonist, Antagonist Predictor (MAAP) was introduced as an advanced ensemble machine learning model that combines XGBoost, Random Forest, and LightGBM to predict the functional roles of agonists, antagonists, and modulators in GPCR-A17 interactions. The model was trained on a dataset of over 3,000 ligands (agonists, antagonists, and modulators) and 6,900 protein–ligand interactions, comprising all three ligand types, sourced from the Guide to Pharmacology, Therapeutic Target Database, and ChEMBL. It demonstrated a strong predictive performance, achieving F1 scores of 0.9179 and 0.7151, AUCs of 0.9766 and 0.8591, and specificities of 0.9703 and 0.8789, respectively, reflecting the overall performance across all classes in the testing and independent ligand validation datasets. A Ki-filtered subset of 4,274 interactions (where Ki is the inhibition constant that quantifies the ligand-binding affinity) improved the F1 scores to 0.9330 and 0.8267 for the testing and independent ligand datasets, respectively. By guiding experimental validation, GPCR-A17 MAAP accelerates drug discovery for various therapeutic targets. The code and data are available on GitHub ( https://github.com/MoreiraLAB/GPCR-A17-MAAP ). This research on GPCRs, particularly the GPCR-A17 subfamily, is significant because of their crucial role in cellular signalling and relevance in developing targeted therapies for complex health conditions. By advancing ligand-receptor interaction predictions (agonists, antagonists, and modulators), this study enhances our understanding of drug-receptor dynamics. These insights can streamline drug discovery, reduce experimental trial-and-error, and accelerate the identification of bioactive compounds for therapeutic applications.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"28 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603430","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}

{"title":"Correction: Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach","authors":"Raheel Hammad, Sownyak Mondal","doi":"10.1186/s13321-025-01046-9","DOIUrl":"https://doi.org/10.1186/s13321-025-01046-9","url":null,"abstract":"<p><b>Correction: Journal of Cheminformatics (2025) 17:95</b> <b>https://doi.org/10.1186/s13321-025-01033-0</b></p><p>Following publication of the original article [1], the authors identified that the author Sownyak Mondal was inadvertently omitted from the *Correspondence section.</p><p>The original article has been corrected.</p><ol data-track-component=\"outbound reference\" data-track-context=\"references section\"><li data-counter=\"1.\"><p>Hammad R, Mondal S (2025) Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach. J Cheminform 17:95. https://doi.org/10.1186/s13321-025-01033-0</p><p>Article PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>Tata Institute of Fundamental Research Hyderabad, Hyderabad, Telangana, 500046, India</p><p>Raheel Hammad & Sownyak Mondal</p></li></ol><span>Authors</span><ol><li><span>Raheel Hammad</span>View author publications<p><span>Search author on:</span><span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Sownyak Mondal</span>View author publications<p><span>Search author on:</span><span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding authors</h3><p>Correspondence to Raheel Hammad or Sownyak Mondal.</p><h3>Publisher's Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.</p>\u0000<p>Reprints and permissions</p><img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjgxIiB3aWR0aD0iNTciIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGcgZmlsbD0ibm9uZSIgZmlsbC1ydWxlPSJldmVub2RkIj48cGF0aCBkPSJtMTcuMzU","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"689 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577891","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}

{"title":"The development of the generative adversarial supporting vector machine for molecular property generation","authors":"Qing Lu","doi":"10.1186/s13321-025-01052-x","DOIUrl":"https://doi.org/10.1186/s13321-025-01052-x","url":null,"abstract":"The generative adversarial network (GAN) is a milestone technique in artificial intelligence, and it is widely used in image generation. However, it has a large hyper-parameter space, which makes it difficult for training. In this work, we propose a new generative model by introducing the supporting vector machine into the GAN architecture. Such modification reduces the hyper-parameter space by half, thus making the training more accessible. The formic acid dimer (FAD) system is studied to examine the generation capacity of the proposed model. The molecular structures, molecular energies and molecular dipole moments are combined as the feature vector to train the model. It is found that the proposed model can generate new feature vectors from scratch, and the generated data agrees well with the ab initio values. In addition, each generated feature vector is unique, so the mode collapse problem is avoided, which is often encountered in the GAN model. The proposed model is extensible to incorporate any molecular properties as the feature vector is established as the direct sum of corresponding component vectors; thus, it is expected that the proposed method will have a wide range of application scenarios. Scientific contribution statement: A generative adversarial algorithm combing supporting vector machine is proposed for the first time to predict molecular properties from scratch, which agrees well with ab initio values. The new model is more efficient than generative adversarial networks, and it is convenient to extend for application in different scenarios.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"81 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578313","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}

{"title":"Implementation of an open chemistry knowledge base with a Semantic Wiki","authors":"Charlotte Neidiger, Tarek Saier, Kai Kühn, Victor Larignon, Michael Färber, Claudia Bizzarri, Helena Šimek Tosino, Laura Holzhauer, Michael Erdmann, An Nguyen, Dean Harvey, Pierre Tremouilhac, Claudia Kramer, Daniel Hansch, Fabian Schönle, Jana Alpin, Maximilian Hartmann, Jérome Wagner, Nicole Jung, Stefan Bräse","doi":"10.1186/s13321-025-01037-w","DOIUrl":"https://doi.org/10.1186/s13321-025-01037-w","url":null,"abstract":"In this work, a concept for an open chemistry knowledge base was developed to integrate chemical research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) to support the collection and structured summary of chemical data contained in publications. We implemented tools for capturing chemical structures in machine-readable formats and designed data forms along with a data model to ensure standardized input and organization of research results. These enhancements allow for effective data comparison and contextual analysis within an expandable Wiki environment. The use of the platform was specifically demonstrated by organizing and comparing research in the area of “CO2 reduction in homogeneous photocatalytic systems,” showcasing its potential to significantly enhance the collaborative collection of research outcomes. Scientific contribution This work shows ways to collaboratively collect and manage subject-specific knowledge in the domain of chemistry via an open database. By integrating cheminformatic tools into Semantic Mediawiki, an established technology for building knowledge databases is made systematically usable for the chemical community. The integration of chemistry-specific workflows and forms allows the mapping of data from current research with links to the original sources. This work is intended to show how gaps in the information system of scientists can be closed without having to use commercial systems.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"4 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568753","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}

{"title":"Shinyscreen: mass spectrometry data inspection and quality checking utility","authors":"Todor Kondić, Anjana Elapavalore, Jessy Krier, Adelene Lai, Hiba Mohammed Taha, Mira Narayanan, Emma L. Schymanski","doi":"10.1186/s13321-025-01044-x","DOIUrl":"https://doi.org/10.1186/s13321-025-01044-x","url":null,"abstract":"Shinyscreen is an R package and Shiny-based web application designed for the exploration, visualization, and quality assessment of raw data from high resolution mass spectrometry instruments. Its versatile list-based approach supports the curation of data starting from either known or “suspected” compounds (compound list-based screening) or detected masses (mass list-based screening), making it adaptable to diverse analytical needs (target, suspect or non-target screening). Shinyscreen can be operated in multiple modes, including as an R package, an interactive command-line tool, a self-documented web GUI, or a network-deployable service. Shinyscreen has been applied in environmental research, database enrichment, and educational initiatives, showcasing its broad utility. Shinyscreen is available in GitLab ( https://gitlab.com/uniluxembourg/lcsb/eci/shinyscreen ) under the Apache License 2.0. The repository contains detailed instructions for deployment and use. Additionally, a pre-configured Docker image, designed for seamless installation and operation is available, with instructions also provided in the main repository. Scientific Contribution: Shinyscreen is a fully open source prescreening application to assist analysts in the high throughput quality control of the thousands of peaks detected in high resolution mass spectrometry experiments. As a vendor-independent, cross operating system application it covers an important niche in open mass spectrometry workflows. Shinyscreen supports quality control of data for further identification or upload of spectra to public data resources, as well as teaching efforts to educate students on the importance of data quality control and rigorous identification methods.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"14 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328890","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}