Journal of Cheminformatics最新文献

χ-SOM: emergent self-organizing maps for large chemical space visualization χ-SOM：用于大型化学空间可视化的紧急自组织地图

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-10-18 DOI: 10.1186/s13321-026-01317-z

Johannes Kaminski,Oliver Koch

{"title":"χ-SOM: emergent self-organizing maps for large chemical space visualization","authors":"Johannes Kaminski,Oliver Koch","doi":"10.1186/s13321-026-01317-z","DOIUrl":"https://doi.org/10.1186/s13321-026-01317-z","url":null,"abstract":"Abstract The exploration and visualization of chemical space using dimensionality reduction (DR) methods are fundamental to cheminformatics, as they enable the exploitation of the similar property principle. However, most methods, including t-SNE and UMAP, struggle to preserve valuable distance and density properties from ambient space in the latent space representation. The emergent self-organizing map (ESOM), an often-ignored extension of the self-organizing map (SOM) algorithm, demonstrates projection behavior that circumvents the disadvantages of these methods. As a suitable implementation of this algorithm for large cheminformatic datasets is not available, we present χ-SOM, the ChemInformatics (ChI)-SOM library, a high-performance framework for training emergent self-organizing maps on large molecular datasets and for interactively exploring the resulting projections. χ-SOM integrates an optimized implementation of the SOM algorithm with CPU and CUDA backends, an on-disk HDF5-based datastore with PyTorch DataLoader support for efficient parallel access to millions of fingerprints, and a graphical user interface tailored to chemical structure visualization and property overlays. We compared our implementation with t-SNE and UMAP on synthetic and chemical data benchmarks that probe density and inter‑cluster distance preservation. On the synthetic benchmarks, ESOM retained both properties where t‑SNE and UMAP did not. On the fingerprint-based equivalents, all three methods performed comparably. χ-SOM was also benchmarked against established SOM libraries across varying dataset sizes and fingerprint lengths, demonstrating competitive or superior performance. As a final proof-of-concept, we successfully trained an ESOM of the ChEMBL33 database (1.7 million compounds; 5.1 million neurons), to our knowledge the largest SOM reported to date, and show that local neighborhood relationships on the map align well with medicinal chemists’ intuition of chemical similarity. χ-SOM thus provides a practical, extensible tool for large scale exploratory analysis of chemical space. ","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"64 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210088","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

Cheminformatics as a core driver of molecular discovery and design 化学信息学是分子发现和设计的核心驱动力

IF 7.9 2区化学

Journal of Cheminformatics Pub Date : 2026-10-16 DOI: 10.1186/s13321-026-01275-6

Barbara Zdrazil, Karina Martinez-Mayorga

引用次数: 0

A diagnostic BRICS fragment-connection framework for infrared-conditioned molecular reconstruction 用于红外条件分子重建的诊断性金砖国家片段连接框架

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-10-12 DOI: 10.1186/s13321-026-01308-0

Limin Yu,Qingyu Zhou,Zhenghui Pan,Jing Wang,Yanqi Zhang

引用次数: 0

StereoMapper: clarifying metabolite identity through stereochemically aware relationship assignment StereoMapper：通过立体化学感知关系分配澄清代谢物身份

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-10-11 DOI: 10.1186/s13321-026-01278-3

Jack McGoldrick,Marco Pagni,Saleh Alwer,Joanne Cooney,Natalia Makosa,Anne Niknejad,Sébastien Moretti,Jadzia Murphy,Filippo Martinelli,Alan Bridge,Ines Thiele,Ronan M. T. Fleming

{"title":"StereoMapper: clarifying metabolite identity through stereochemically aware relationship assignment","authors":"Jack McGoldrick,Marco Pagni,Saleh Alwer,Joanne Cooney,Natalia Makosa,Anne Niknejad,Sébastien Moretti,Jadzia Murphy,Filippo Martinelli,Alan Bridge,Ines Thiele,Ronan M. T. Fleming","doi":"10.1186/s13321-026-01278-3","DOIUrl":"https://doi.org/10.1186/s13321-026-01278-3","url":null,"abstract":"Abstract Inaccuracies in metabolite cross-mapping frequently introduce errors into genome-scale metabolic models (GEMs). A key challenge lies in distinguishing truly identical metabolites from closely related structural variants, such as stereoisomers, across biochemical databases. StereoMapper addresses this issue by establishing stereochemically aware relationships that clarify metabolite identity. It operates entirely on structural information, using molecular structures to infer equivalence and define relationships. StereoMapper was implemented using RDKit (2025.3.3) and Open Babel-wheel (3.1.1.22) which provides the core functionality of the Open Babel software, and benchmarked across four curated control datasets to evaluate performance in relationship classification (enantiomer, diastereomer, stereo-resolution pairs, and protomer). The pipeline was subsequently applied to 1.3 million molecular structures from multiple metabolic databases to generate equivalent isomeric sets (groups of structures identified as equivalent by StereoMapper). These sets were compared against the human subset of MetaNetX, a context-aware reference, to assess concordance and investigate disagreements. On curated positive control sets containing only positive examples across multiple relationship classes, StereoMapper achieved mean precision, recall, and F1-scores of 93%, 98%, and 95%, respectively; following optimisation, precision and F1 increased to 99% and 98%, while recall remained at 98%. Application to the 1.3 million structures produced over 339,000 stereochemically defined relationships between molecular structures. Comparison with the human subset of MetaNetX assignments showed an overall grouping concordance of 93.5%. The remaining 6.5% of differences were mainly associated with protomer relationships (52%) and stereo-resolution pairs (37%), while 21% involved complex or ambiguous cases. ","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"56 2 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151183","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

Hierarchical generative modeling for the design of multi-component systems 多构件系统设计的层次生成建模

IF 7.9 2区化学

Journal of Cheminformatics Pub Date : 2026-10-11 Epub Date: 2026-10-12 DOI: 10.1186/s13321-026-01313-3

Rhyan Barrett, Robin Curth, Julia Westermayr

{"title":"Hierarchical generative modeling for the design of multi-component systems","authors":"Rhyan Barrett, Robin Curth, Julia Westermayr","doi":"10.1186/s13321-026-01313-3","DOIUrl":"10.1186/s13321-026-01313-3","url":null,"abstract":"<div><p>The functionality of catalysts, enzymes, and supramolecular assemblies emerges not from individual molecules alone, but from the subtle interplay between multiple components arranged in complex systems. Designing such systems is a grand challenge: the combinatorial explosion of possible chemical compositions and spatial arrangements makes brute-force exploration infeasible, while many current generative approaches remain limited to isolated molecules. In this work, we introduce a hierarchical generative optimization framework that overcomes this barrier by coupling a genetic algorithm for configurational search with a generative model for molecular design. This closed-loop approach enables simultaneous refinement of geometry and composition, efficiently steering discovery toward systems with targeted functionality. As a proof of concept, we design catalytic environments for the Claisen rearrangement of p-tolyl ether by optimizing surrounding components around a fixed reference transition-state geometry. Despite this constraint during the search phase, post-hoc validation via Climbing-Image Nudged Elastic Band calculations, with the environment held fixed, indicates a reduction of the activation barrier by about 30%. Beyond this example, the framework applies to systems in which a well-defined central structure is stabilized by non-covalently interacting components, such as supramolecular hosts and local catalytic environments. </p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01313-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151181","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

The NERDD microservices platform for deploying machine learning models in drug discovery 用于在药物发现中部署机器学习模型的NERDD微服务平台

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-10-06 DOI: 10.1186/s13321-026-01312-4

Steffen Hirte,Vincent-Alexander Scholz,Johannes Kirchmair

引用次数: 0

NephroTox AI: an explainable machine learning framework with uncertainty quantification for drug-induced nephrotoxicity prediction NephroTox AI：一个可解释的机器学习框架，具有不确定性量化，用于药物引起的肾毒性预测

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-10-05 DOI: 10.1186/s13321-026-01299-y

Jixing Liu,Jiabing Guo,Henry H. Y. Tong,Shu Li

引用次数: 0

Benchmarking descriptor-based AI approaches for predicting the BBB permeability of drug-like xenobiotics 对基于描述符的人工智能方法进行基准测试，以预测类药物外源药物的血脑屏障通透性

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-09-25 DOI: 10.1186/s13321-026-01309-z

Nicolina Sciaraffa,Martina Valentino,Maria Rita Gulotta,Claudia Coronnello,Francesco Lopresti,Ugo Perricone

引用次数: 0

Correction: Scaffold‑based evaluation metrics for fair comparison of molecular generators 更正：用于公平比较分子发生器的基于支架的评价指标

IF 7.9 2区化学

Journal of Cheminformatics Pub Date : 2026-09-24 DOI: 10.1186/s13321-026-01304-4

Valeriia Fil, Remco L. Van Den Broek, Martin Šicho, Ivan Čmelo, M. Isabel Agea, Willem Jespers, Gerard J. P. Van Westen, Daniel Svozil

引用次数: 0

Integrating multi-criteria decision-making and machine learning for fingerprint selection and virtual screening of Acetylcholinesterase inhibitors 结合多准则决策和机器学习的指纹选择和虚拟筛选乙酰胆碱酯酶抑制剂

IF 8.6 2区化学

Journal of Cheminformatics Pub Date : 2026-09-23 DOI: 10.1186/s13321-026-01300-8

Moruf Adedeji Adeagbo,Waliyullah Abefe Ibrahim,Sabrina de Azevedo Silveira

引用次数: 0