Journal of Cheminformatics最新文献

{"title":"Anyone Can Dock: an online molecular docking tool for everyone","authors":"Kowit Hengphasatporn,Thanthapatra Bunchuay,Lian Duan,Yasuteru Shigeta","doi":"10.1186/s13321-026-01306-2","DOIUrl":"https://doi.org/10.1186/s13321-026-01306-2","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"1 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022175","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 general deep learning method based on substrate information for genome-scale mining of natural product biosynthetic enzymes","authors":"Yangyang Li,Yuntong Feng,Zhiqiang Wei,Xiangzhao Mao,Hong Jiang,Xiancong Hou,Dawei Yang,Wenzheng Han,Hao Liu","doi":"10.1186/s13321-026-01307-1","DOIUrl":"https://doi.org/10.1186/s13321-026-01307-1","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"95 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022174","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":"ncAA-RepDistill: a chemistry-native SMILES representation for permeability prediction and local edit ranking of non-canonical cyclic peptides","authors":"Qiule Yu,Zhixing Zhang,Xiang Li,Weihua Li,Guixia Liu,Yun Tang","doi":"10.1186/s13321-026-01311-5","DOIUrl":"https://doi.org/10.1186/s13321-026-01311-5","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"38 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021780","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":"DockM8: an all-in-one open-source platform for consensus virtual screening in drug design","authors":"Antoine Lacour,&nbsp;Hamza Agha,&nbsp;Anna K. H. Hirsch,&nbsp;Andrea Volkamer","doi":"10.1186/s13321-026-01287-2","DOIUrl":"10.1186/s13321-026-01287-2","url":null,"abstract":"<div><p>Virtual screening remains a critical step in structure-based drug design, yet variability in docking algorithms and scoring functions often limits its reliability. To address this challenge, we introduce DockM8, an open-source platform for consensus virtual screening that integrates pocket detection, ligand preparation, docking, rescoring, and consensus ranking within a single modular workflow. DockM8 supports five docking engines, 17 pose-selection methods, 17 scoring functions (SFs), and five consensus methods, all accessible through both a graphical user interface (GUI) and a Python application programming interface (API). Systematic evaluation on the DEKOIS 2.0, DUD-E, and Lit-PCBA datasets yielded median relative enrichments of 100%, 82.59%, and 8.85%, respectively, frequently matching or outperforming state-of-the-art approaches. Our findings further reveal that no pose-selection or consensus strategy universally excels, emphasizing the need for tailored, target-specific workflows. DockM8 is freely available under the GNU General Public License at https://github.com/DrugBud-Suite/DockM8.</p><p> <b>Scientific contribution</b></p><p>DockM8 is, to our knowledge, the most comprehensive open-source platform to integrate the complete consensus structure-based virtual screening (SBVS) pipeline within a single, graphical-interface-driven tool, yielding over 55 million configurable protocols, a breadth unmatched by previously published consensus docking software. Through systematic multi-benchmark evaluation, we demonstrate that no single pose-selection or consensus strategy generalizes across targets, motivating a target-specific workflow-selection paradigm in place of prevailing one-size-fits-all heuristics. By delivering this tunability through a graphical interface rather than scripts, DockM8 brings rigorous, reproducible consensus virtual screening within reach of non-specialist medicinal chemists.</p><h3>Graphic Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01287-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021778","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}

{"title":"LIDR-TB: large language model-integrated platform for traceable drug repurposing in tuberculosis","authors":"ShanShan Hu,Song Jie,Amin Ullah,Jiajia Dong,Xin Zheng,Xingyun Liu,Hui Zong,Jiao Wang,Xiaoyu Li,Bairong Shen","doi":"10.1186/s13321-026-01305-3","DOIUrl":"https://doi.org/10.1186/s13321-026-01305-3","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"40 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021775","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":"Analysis of structural errors in the AlphaFold DB v4 and v6","authors":"Lukáš Bohuš,Tomáš Svoboda,Ondřej Schindler","doi":"10.1186/s13321-026-01285-4","DOIUrl":"https://doi.org/10.1186/s13321-026-01285-4","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"396 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021777","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":"Per-residue optimisation of protein structures: rapid alternative to local optimisation with constrained alpha carbons","authors":"Ondřej Schindler,Tomáš Svoboda,Gabriela Bučeková,Radka Svobodová","doi":"10.1186/s13321-026-01303-5","DOIUrl":"https://doi.org/10.1186/s13321-026-01303-5","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"398 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021527","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":"G-screen: scalable protein-aware virtual screening through flexible ligand alignment","authors":"Nuri Jung,Hahnbeom Park,Jinsol Yang,Chaok Seok","doi":"10.1186/s13321-026-01297-0","DOIUrl":"https://doi.org/10.1186/s13321-026-01297-0","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"64 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021573","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":"patRoon 3.0: enhanced non-target analysis workflows with ion mobility separation and other major improvements","authors":"Rick Helmus,Jan C. Specker,Christine Gallampois,Emma L. Schymanski","doi":"10.1186/s13321-026-01302-6","DOIUrl":"https://doi.org/10.1186/s13321-026-01302-6","url":null,"abstract":"Abstract Non-target analysis (NTA) is used to identify or annotate many chemicals in complex samples, with applications in environmental analysis, metabolomics, exposomics and others. Chemical analysis is predominantly performed with chromatography coupled to high resolution mass spectrometry (HRMS). Although this technique can screen numerous chemicals simultaneously in complex sample material, the retention time and mass data obtained is often insufficient to completely separate and unequivocally identify chemicals of interest. Ion mobility separation (IMS) is increasingly used as an additional separation technique, providing complementary collision cross section (CCS) data to aid annotation. This work introduces the third major release of patRoon , an established open source platform to perform comprehensive NTA, which was upgraded to support the data complexity of IMS-HRMS instruments and integrate the benefits of this technique throughout the complete data processing workflow. This is implemented through msdata , a raw data interface optimized for complex IMS-HRMS data, and the simple, flexible and fast piek and greedy algorithms to obtain IMS-HRMS resolved features. These are combined with new functionality and existing algorithms in patRoon to obtain features and assign mobility and CCS data via two mobility assignment workflows. The IMS data assigned to features can be used throughout patRoon to enhance annotation by matching empirical or predicted CCS data and improve prioritization and interpretation of NTA data. Evaluation of this work demonstrated ease of use combined with optimized runtime performance, while the two mobility assignment workflows were complementary in terms of complexity, algorithm compatibility and performance, with each revealing unique data. Nevertheless, when applied to samples of different complexities and acquired with different instruments, both workflows resulted in CCS data that was comparable to vendor software. Thus, patRoon 3.0 brings comprehensive and vendor agnostic workflows for IMS-HRMS based NTA. Since most of the upgrades are not limited to IMS data, patRoon 3.0 (available at https://rickhelmus.github.io/patRoon/ ) has the potential to enhance data processing and outcomes in a wide variety of NTA studies. Scientific contribution The complete patRoon workflow was upgraded to make it compatible with ion mobility separation, extending the analytical strengths of ion mobility separation to a wide variety of non-target analysis studies. A highly optimized raw data interface and data processing algorithms were developed to effectively process complex ion mobility resolved data, which also improved the non-target analysis workflows without ion mobility data. Since the workflows developed here yield similar results to vendor software, patRoon 3.0 can act as unifying, vendor agnostic platform to improve the processing, consistency and comparability of ion mobility resolved data.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"8 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021574","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":"BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules","authors":"Lukas M. Sigmund,&nbsp;Michele Assante,&nbsp;Matthew Ball,&nbsp;Mikhail Kabeshov","doi":"10.1186/s13321-026-01283-6","DOIUrl":"10.1186/s13321-026-01283-6","url":null,"abstract":"<div><p>Data-driven chemistry relies on robust, efficient, and reproducible pipelines for generating digital molecular representations. While comprehensive software for global molecular descriptors is widely available, the calculation of local atom and bond features remains fragmented across disparate tools with inconsistent interfaces. Here, we introduce the BOnd aNd Atom FeaturIzer and Descriptor Extractor (BONAFIDE), a Python package designed to standardize the generation of local descriptors. BONAFIDE provides a unified application programming interface that integrates 11 external computational chemistry and cheminformatics packages, including RDKit, MORFEUS, and Multiwfn as well as quantum chemical engines like xtb and Psi4 for single-point energy computations. The software currently supports the calculation of 590 distinct atom and bond features derived from either 2D or 3D molecular representations and electronic structure data. Key functionalities include flexible input format processing as well as output customization, seamless atom and bond feature calculation across all implemented descriptors, transparent hyperparameter tracking, comprehensive logging, and the possibility to implement custom featurizers within the BONAFIDE ecosystem. We also include runtime statistics for featurization and a case study that demonstrates how the tool is used for the featurization of amines through a multistep featurization pipeline. Current limitations and future development directions are discussed.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div><p><b>Scientific contribution</b></p><p>This work introduces a Python software package denoted BONAFIDE, which facilitates the calculation of descriptors for atoms and bonds in molecules. By providing a unified interface that bridges diverse cheminformatics and quantum chemistry tools, this package streamlines data generation pipelines and accelerates the development of robust machine learning models. BONAFIDE is available free of charge as open-source software under the Apache 2.0 license and can be obtained from GitHub (https://github.com/MolecularAI/atom-bond-featurizer).</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01283-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926877","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}