Journal of Cheminformatics最新文献

{"title":"ELNdataBridge: facilitating data exchange and collaboration by linking Electronic Lab Notebooks via API","authors":"Martin Starman,&nbsp;Fabian Kirchner,&nbsp;Martin Held,&nbsp;Catriona Eschke,&nbsp;Sayed-Ahmad Sahim,&nbsp;Regine Willumeit-Römer,&nbsp;Nicole Jung,&nbsp;Stefan Bräse","doi":"10.1186/s13321-025-01024-1","DOIUrl":"10.1186/s13321-025-01024-1","url":null,"abstract":"<div><p>Electronic Lab Notebooks (ELNs) have become indispensable tools for modern research laboratories, facilitating data management, collaboration, and documentation of scientific experiments. However, the proliferation of diverse ELN platforms poses challenges for researchers who need to seamlessly exchange data between different systems. In this paper, we present ELNdataBridge, a novel server-based solution designed to address this challenge by providing a flexible adapter for interfacing and synchronising data between disparate ELN platforms. ELNdataBridge leverages Python APIs to interact with the underlying data structures of various ELN systems, enabling smooth transfer of information between them. The system offers a user-friendly interface that allows researchers to map and configure the transfer of single values and entry types between different ELNs, thereby facilitating interoperability and data exchange. The suitability and efficiency of the developed software was shown by a first demonstrator, enabling the exchange of data from Chemotion ELN and Herbie, and therewith the connection of information with a focus on chemistry and materials sciences.</p><p><b>Scientific contribution:</b> To the best of our knowledge, a method enabling the interoperable exchange of information between different ELNs, as described here, has not yet been reported. Given the increasing number of scientists using ELNs and their reliance on discipline-specific platforms, this work proposes a solution to overcome the current limitations related to ELN interoperability.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01024-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136971","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":"Context-dependent similarity searching for small molecular fragments","authors":"Atsushi Yoshimori,&nbsp;Jürgen Bajorath","doi":"10.1186/s13321-025-01032-1","DOIUrl":"10.1186/s13321-025-01032-1","url":null,"abstract":"<div><p>Similarity searching is a mainstay in cheminformatics that is generally used to identify compounds with desired properties. For small molecular fragments, similarity calculations based on standard descriptors often have limited utility for establishing meaningful similarity relationships due to feature sparseness. As an alternative, we have adapted the concept of context-depending word pair similarity from natural language processing to evaluate similarity relationships between substituents (R-groups) taking latent characteristics into account. Context-dependent similarity assessment is based on vector embeddings as fragment representations generated using neural networks. With active analogue series as a model system to establish a global structure–activity context, we demonstrate that this approach is applicable to systematic similarity searching for substituents and increases the performance of standard descriptor representations. Context-dependent similarity searching is capable of detecting remote and functionally relevant similarity relationships between substituents. Alternative search queries are introduced focusing on individual substituents within a global substituent context or individual sequences of substituents establishing a local context. For similarity searching, different structural or structure–property contexts can be established, providing opportunities for various applications.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01032-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136979","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":"Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN)","authors":"Achmad Anggawirya Alimin,&nbsp;Kattariya Srasamran,&nbsp;Wanutchaya Yuenyong,&nbsp;Ampira Charoensaeng,&nbsp;Bor-Jier Shiau,&nbsp;Uthaiporn Suriyapraphadilok","doi":"10.1186/s13321-025-01027-y","DOIUrl":"10.1186/s13321-025-01027-y","url":null,"abstract":"<div><p>Predicting surfactant adsorption using the currently available isotherm model is limited to one or two independent variables: equilibrium concentration and temperature. This study aims to develop an adsorption model that includes molecular features, testing conditions, and solid properties in the model. A Physics-Informed Neural Network (PINN) was structured by integrating adsorption isotherm into artificial neural networks (ANN). The model was trained using a dataset containing 56 adsorption isotherms and 20 types of anionic and nonionic surfactants under various conditions with sand and silica oxide as their solids. The surfactants were quantified using sets of descriptors generated from molecular counting, charge distribution, and Conductor-like Screening Model (COSMO) screened charge density. The COSMO-screened charge density descriptors provide the highest accuracy in representing the surfactant molecule. The interpretation of molecular structure effect and surfactant-solid interaction described using COSMO-screened charge density showed that adsorption between the surfactant and solid media involves hydrogen bonding and hydrophobic interaction. The PINN model achieves high accuracy with 93% training and 85% validation with fivefold cross-validation. Later, the model was evaluated and used to generate an adsorption isotherm and predict unseen surfactant adsorption. Adsorption prediction with unseen surfactants showed high accuracy with the surfactant for familiar structure (RMSE 0.07 mg/g) and promising profile for the whole new structure (RMSE 2.95 mg/g). <b>Scientific contribution</b> This study advances the field by integrating COSMO-screened charge density descriptors into a physics-informed deep learning model to predict surfactant adsorption isotherms, accounting for molecular features, testing conditions, and solid properties. The incorporation of COSMO-screened charge density offers a novel approach to accurately represent surfactant molecules, enabling accurate prediction of their adsorption behavior. This approach extends conventional models, which are often limited to empirical parameters or fewer variables. This physics-informed framework significantly enhances the understanding of surfactant-solid interactions and offers a robust predictive tool for optimizing surfactant formulations, aiming to minimize adsorption losses in chemical enhanced oil recovery and environmental remediation.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01027-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136984","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":"Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models","authors":"Cecile Valsecchi,&nbsp;Jose A. Arjona-Medina,&nbsp;Natalia Dyubankova,&nbsp;Ramil Nugmanov","doi":"10.1186/s13321-025-01004-5","DOIUrl":"10.1186/s13321-025-01004-5","url":null,"abstract":"<div><p>The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01004-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117787","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":"A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF","authors":"Qingliang Li,&nbsp;Sunghwan Kim,&nbsp;Leonid Zaslavsky,&nbsp;Tiejun Cheng,&nbsp;Bo Yu,&nbsp;Evan E. Bolton","doi":"10.1186/s13321-025-01017-0","DOIUrl":"10.1186/s13321-025-01017-0","url":null,"abstract":"<div><p>Named entities, such as chemicals/drugs, genes/proteins, and diseases, and their associations are not only important components of biomedical literature, but also the foundation of creating biomedical knowledgebases and knowledge graphs. This work addresses the challenges of expressing co-occurrence associations between named entities extracted from a biomedical literature corpus in a machine-readable format. We developed a Resource Description Framework (RDF) data model and integrated it into the PubChemRDF resource, which is freely accessible and publicly available. The developed co-occurrence data model was populated into a triplestore with named entities and their associations derived from text mining of millions of biomedical references found in PubMed. The utility of the data model was demonstrated through multiple use cases. Together with meta-data modeling of the references including the information about the author, journal, grant, and funding agency, this data model allows researchers to address pertinent biomedical questions through SPARQL queries and helps to exploit biomedical knowledge in various user perspectives and use cases.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01017-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108437","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":"Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution","authors":"Mengyang Qu,&nbsp;Gyanendra Sharma,&nbsp;Naoki Wada,&nbsp;Hisaki Ikebata,&nbsp;Shigeyuki Matsunami,&nbsp;Kenji Takahashi","doi":"10.1186/s13321-025-01018-z","DOIUrl":"10.1186/s13321-025-01018-z","url":null,"abstract":"<div><p>Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01018-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100275","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":"Advantages of two quantum programming platforms in quantum computing and quantum chemistry","authors":"Pei-Hua Wang,&nbsp;Wei-Yeh Wu,&nbsp;Che-Yu Lee,&nbsp;Jia-Cheng Hong,&nbsp;Yufeng Jane Tseng","doi":"10.1186/s13321-025-01026-z","DOIUrl":"10.1186/s13321-025-01026-z","url":null,"abstract":"<div><p>Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://github.com/wangpeihua1231/quantum-programming-platform.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01026-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090960","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":"Assessing interaction recovery of predicted protein-ligand poses","authors":"David Errington,&nbsp;Constantin Schneider,&nbsp;Cédric Bouysset,&nbsp;Frédéric A. Dreyer","doi":"10.1186/s13321-025-01011-6","DOIUrl":"10.1186/s13321-025-01011-6","url":null,"abstract":"<div><p>The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.</p><p><b>Scientific Contribution</b> The interaction analysis used in this study is provided as a python package at https://github.com/Exscientia/plif_validity.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01011-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090959","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":"Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction","authors":"Sumin Ha,&nbsp;Dongmin Bang,&nbsp;Sun Kim","doi":"10.1186/s13321-025-01012-5","DOIUrl":"10.1186/s13321-025-01012-5","url":null,"abstract":"<div><p>Toxicity is a critical hurdle in drug development, often causing the late-stage failure of promising compounds. Existing computational prediction models often focus on single-organ toxicity. However, avoiding toxicity of an organ, such as reducing gastrointestinal side effects, may inadvertently lead to toxicity in another organ, as seen in the real case of rofecoxib, which was withdrawn due to increased cardiovascular risks. Thus, simultaneous prediction of multi-organ toxicity is a desirable but challenging task. The main challenges are (1) the variability of substructures that contribute to toxicity of different organs, (2) insufficient power of molecular representations in diverse perspectives, and (3) explainability of prediction results especially in terms of substructures or potential toxicophores. To address these challenges with multiple strategies, we developed FATE-Tox, a novel multi-view deep learning framework for multi-organ toxicity prediction. For variability of substructures, we used three fragmentation methods such as BRICS, Bemis-Murcko scaffolds, and RDKit Functional Groups to formulate fragment-level graphs so that diverse substructures can be used to identify toxicity for different organs. For insufficient power of molecular representations, we used molecular representations in both 2D and 3D perspectives. For explainability, our fragment attention transformer identifies potential 3D toxicophores using attention coefficients. </p><p><b>Scientific contribution</b>: Our framework achieved significant improvements in prediction performance, with up to 3.01% gains over prior baseline methods on toxicity benchmark datasets from MoleculeNet (BBBP, SIDER, ClinTox) and TDC (DILI, Skin Reaction, Carcinogens, and hERG), while the multi-task learning approach further enhanced performance by up to 1.44% compared to the single-task learning framework that had already surpassed these baselines. Additionally, attention visualization aligning with literature contributes to greater transparency in predictive modeling. Our approach has the potential to provide scientists and clinicians with a more interpretable and clinically meaningful tool to assess systemic toxicity, ultimately supporting safer and more informed drug development processes.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01012-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944377","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":"Addressing standardization and semantics in an electronic lab notebook for multidisciplinary use: LabIMotion","authors":"Chia-Lin Lin,&nbsp;Pei-Chi Huang,&nbsp;Christof Wöll,&nbsp;Patrick Théato,&nbsp;Christian Kübel,&nbsp;Lena Pilz,&nbsp;Nicole Jung,&nbsp;Stefan Bräse","doi":"10.1186/s13321-025-01021-4","DOIUrl":"10.1186/s13321-025-01021-4","url":null,"abstract":"<div><p>This work presents the LabIMotion extension for the Chemotion Electronic Lab Notebook (ELN), expanding its capabilities from organic chemistry to support interdisciplinary research and enabling the description of workflows. LabIMotion enhances documentation by introducing customizable components structured across three levels—<i>Elements</i>, <i>Segments</i>, and <i>Datasets</i>—enabling flexible, hierarchical organization and reuse of data. Through the integration of links to ontologies, the extension ensures precise, machine-readable data, promoting interoperability and adherence to FAIR principles. The extension features an intuitive, user-friendly interface that allows researchers to easily create new ELN content by leveraging a set of customizable, generic methods. Scientists can set up new data fields, can link data fields, or establish workflows, and the extension translates those needs directly into usable functionality at their command. Through this high degree of flexibility, a wide range of specific research needs can be met. The LabIMotion Hub plays a crucial role in distributing and updating components, fostering standardization, and enabling collaborative development within scientific communities. These advancements significantly improve the ELN's adaptability, usability, and relevance across various research disciplines.</p><p><b>Scientific contribution</b></p><p>This work demonstrates how research data management systems can be designed to support discipline-specific requirements in chemistry research while offering a high flexibility and interoperability to deal with interdisciplinary work. The developed software, LabIMotion, offers a versatile approach for integrating novel research aspects into a research data environment, fostering bottom-up processes for defining schemas and standardizing scientific workflows. In particular, the software’s support for community-driven extensions, combined with a clear definition of content and its assignment to ontology terms, provides unique advantages for creating adaptable tools suited to the complexities of the scientific environment.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01021-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944376","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}