Journal of Cheminformatics最新文献

{"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}

{"title":"Generalizable, fast, and accurate DeepQSPR with fastprop","authors":"Jackson W. Burns,&nbsp;William H. Green","doi":"10.1186/s13321-025-01013-4","DOIUrl":"10.1186/s13321-025-01013-4","url":null,"abstract":"<div><p>Quantitative Structure–Property Relationship studies (QSPR), often referred to interchangeably as QSAR, seek to establish a mapping between molecular structure and an arbitrary target property. Historically this was done on a target-by-target basis with new descriptors being devised to <i>specifically</i> map to a given target. Today software packages exist that calculate thousands of these descriptors, enabling general modeling typically with classical and machine learning methods. Also present today are learned representation methods in which deep learning models generate a target-specific representation during training. The former requires less training data and offers improved speed and interpretability while the latter offers excellent generality, while the intersection of the two remains under-explored. This paper introduces <span>fastprop</span>, a software package and general Deep-QSPR framework that combines a cogent set of molecular descriptors with deep learning to achieve state-of-the-art performance on datasets ranging from tens to tens of thousands of molecules. <span>fastprop</span> provides both a user-friendly Command Line Interface and highly interoperable set of Python modules for the training and deployment of feedforward neural networks for property prediction. This approach yields improvements in speed and interpretability over existing methods while statistically equaling or exceeding their performance across most of the tested benchmarks. <span>fastprop</span> is designed with Research Software Engineering best practices and is free and open source, hosted at github.com/jacksonburns/fastprop.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01013-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944189","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":"Generating diversity and securing completeness in algorithmic retrosynthesis","authors":"Florian Mrugalla,&nbsp;Christopher Franz,&nbsp;Yannic Alber,&nbsp;Georg Mogk,&nbsp;Martín Villalba,&nbsp;Thomas Mrziglod,&nbsp;Kevin Schewior","doi":"10.1186/s13321-025-00981-x","DOIUrl":"10.1186/s13321-025-00981-x","url":null,"abstract":"<p>Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for assembling such predictions to a full synthesis plan that, starting from simple building blocks, produces a given target molecule, a procedure known as retrosynthesis. Objective functions for this task are hard to define and context-specific. In order to generate a diverse set of synthesis plans for chemists to select from, we capture the concept of diversity in a novel chemical diversity score (CDS). Our experiments show that our algorithm outperforms the algorithm predominantly employed in this domain, Monte-Carlo Tree Search, with respect to diversity in terms of our score as well as time efficiency.</p><p>We adapt Depth-First Proof-Number Search (DFPN) (Please refer to https://github.com/Bayer-Group/bayer-retrosynthesis-search for the accompanying source code.) and its variants, which have been applied to retrosynthesis before, to produce a set of solutions, with an explicit focus on diversity. We also make progress on understanding DFPN in terms of completeness, i.e., the ability to find a solution whenever there exists one. DFPN is known to be incomplete, for which we provide a much cleaner example, but we also show that it is complete when reinforced with a threshold-controlling routine from the literature.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00981-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944190","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":"The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023","authors":"Murat Koçak,&nbsp;Zafer Akçalı","doi":"10.1186/s13321-025-00988-4","DOIUrl":"10.1186/s13321-025-00988-4","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is used extensively. Therefore, this study aims to systematically analyze the scientific literature on the application of AI in drug discovery and development to understand the evolution, trends, and key contributors within this rapidly growing field. By leveraging various bibliometric indicators and visualization techniques, we seek to explore the growth patterns, influential authors and institutions, collaboration networks, and emerging research trends within this domain. Bibliometric and network analysis methods (co-occurrence, co-authorship, and collaboration, etc.) were used to achieve this goal. Bibliometric visualization tools such as Bibliometrix R package software, VOSviewer, and Litmaps were used for comprehensive data analysis. Scientific publications on AI in drug discovery and development were retrieved from the Web of Science Core Collection (WoS CC) database covering 1990–2023. In addition to visualization programs, the InCites database was also used for analysis and visualization. A total of 4059 scientific publications written by 13,932 authors and published in 1071 journals were included in the analysis. The results reveal that the most prolific authors are Ekins (n = 67), Schneider (n = 52), Hou Tj (n = 43), and Cao Ds (n = 34), while the most active institutions are the “Chinese Academy of Science” and “University of California.” The leading scientific journals are “Journal of Chemical Information and Modelling,” “Briefings in Bioinformatics,” and “Journal of Cheminformatics.” The most frequently used author keywords include “protein folding,” “QSAR,” “gene expression data,” “coronavirus,” and “genome rearrangement.” The average number of citations per scientific publication is 28.62, indicating a high impact of research in this field. A significant increase in publications was observed after 2014, with a peak in 2022, followed by a slight decline. International collaboration accounts for 28.06% of the publications, with the USA and China leading in both productivity and influence. The study also identifies key funding organizations, such as the National Natural Science Foundation of China (NSFC) and the United States Department of Health &amp; Human Services, which have significantly supported advancements in this field. In conclusion, this study highlights the transformative role of AI in drug discovery and development, showcasing its potential to accelerate innovation and improve efficiency. The findings provide valuable insights into the current state of research, emerging trends, and future directions, offering a roadmap for researchers, industry professionals, and policymakers to further explore and leverage AI technologies in this domain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Scientific contribution&lt;/b&gt;This study provides a comprehensive bibliometric analysis of 4,059 scientific publications (1990–2023) to map the evolution, trends, and key contrib","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00988-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920516","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":"Predicting inhibitors of OATP1B1 via heterogeneous OATP-ligand interaction graph neural network (HOLIgraph)","authors":"Mehrsa Mardikoraem,&nbsp;Joelle N. Eaves,&nbsp;Theodore Belecciu,&nbsp;Nathaniel Pascual,&nbsp;Alexander Aljets,&nbsp;Bruno Hagenbuch,&nbsp;Erik M. Shapiro,&nbsp;Benjamin J. Orlando,&nbsp;Daniel R. Woldring","doi":"10.1186/s13321-025-01020-5","DOIUrl":"10.1186/s13321-025-01020-5","url":null,"abstract":"<div><p>Organic anion transporting polypeptides (OATPs) are membrane transporters crucial for drug uptake and distribution in the human body. OATPs can mediate drug-drug interactions (DDIs) in which the interaction of one drug with an OATP impairs the uptake of another drug, resulting in potentially fatal pharmacological effects. Predicting OATP-mediated DDIs is challenging, due to limited information on OATP inhibition mechanisms and inconsistent experimental OATP inhibition data across different studies. This study introduces Heterogeneous OATP-Ligand Interaction Graph Neural Network (HOLIgraph), a novel computational model that integrates molecular modeling with a graph neural network to enhance the prediction of drug-induced OATP inhibition. By combining ligand (i.e., drug) molecular features with protein-ligand interaction data from rigorous docking simulations, HOLIgraph outperforms traditional DDI prediction models which rely solely on ligand molecular features. HOLIgraph achieved a median balanced accuracy of over 90 percent when predicting inhibitors for OATP1B1, significantly outperforming purely ligand-based models. Beyond improving inhibition prediction, the data used to train HOLIgraph can enable the characterization of protein residues involved in inhibitory drug-OATP interactions. We identified certain OATP1B1 residues that preferentially interact with inhibitors, including I46 and K49. We anticipate such interaction information will be valuable to future structural and mechanistic investigations of OATP1B1.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01020-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908853","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":"Application of 3D atom pair map in an attention model for enhanced drug virtual screening","authors":"Gina Ryu,&nbsp;Wankyu Kim","doi":"10.1186/s13321-025-01023-2","DOIUrl":"10.1186/s13321-025-01023-2","url":null,"abstract":"<p>This study demonstrates the utility of a novel molecular representation, 3D APM and a deep learning model based on it for virtual screening, suggesting that many other prediction models would also benefit from adopting APM. An open-source script to generate 3D APM is available at https://github.com/rimeless/APM</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01023-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908751","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":"Prediction of blood–brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks","authors":"Nikoletta-Maria Koutroumpa,&nbsp;Andreas Tsoumanis,&nbsp;Haralambos Sarimveis,&nbsp;Iseult Lynch,&nbsp;Georgia Melagraki,&nbsp;Antreas Afantitis","doi":"10.1186/s13321-025-01007-2","DOIUrl":"10.1186/s13321-025-01007-2","url":null,"abstract":"<div><p>In this study, we introduce a novel approach for predicting two key drug properties, blood–brain barrier (BBB) permeability and human intestinal absorption via Caco-2 permeability. Our methodology centers around a specialized neural network, the atom transformer-based Message Passing Neural Network (MPNN), which we have combined with contrastive learning techniques to enhance the process of representing and embedding molecular structures for more accurate property prediction. These innovative models focus on predicting BBB and Caco-2 permeability -two critical factors in drug absorption and distribution- which fall under the broader scope of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. The models are readily accessible online through the Enalos Cloud Platform which offers a user-friendly, AI-powered, ready-to-use web service that significantly streamlines the drug design process, enabling users to easily predict and understand the behavior of potential drug compounds within the human body.</p><p><b>Scientific Contribution</b> Our study combines an atom-attention Message Passing Neural Network (AA-MPNN) with contrastive learning (CL), which significantly improves predictive accuracy. Our model leverages self-supervised learning to expand the chemical space used in training and self-attention mechanisms to focus on critical molecular features, enhancing both model accuracy and interpretability. Additionally, the ready-to-use web service based on our model democratizes access to predictive tools for the scientific and regulatory communities.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01007-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904854","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}