Molecular Informatics最新文献

{"title":"Molecular Modeling and Chemoinformatics in Ukraine.","authors":"Dmytro M Volochnyuk, Serhiy V Ryabukhin","doi":"10.1002/minf.70034","DOIUrl":"10.1002/minf.70034","url":null,"abstract":"<p><p>The special issue collects recent contributions from Ukrainian researchers, both from academia and industry, in the field of chemoinformatics. It contains 6 publications from leading Ukrainian scientists in the field. These articles representatively demonstrated a wide landscape of chemoinformatics in Ukraine and its deep integration into the global one.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70034"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADME-DTI: Augmented Deep Meta Ensemble for Drug-Target Interaction Prediction.","authors":"Tariq Sha'ban, Ahmad M Mustafa, Mostafa Z Ali, Talal Z Ali","doi":"10.1002/minf.70033","DOIUrl":"10.1002/minf.70033","url":null,"abstract":"<p><p>Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identifying and analyzing these interactions is often resource-intensive, requiring extensive experimentation to evaluate the binding relationships between numerous drugs and their respective targets. This complexity is further compounded by the fact that a drug can inhibit multiple targets, and a target may also bind to various drugs. To address these issues, advanced deep learning models have been introduced as promising tools, offering the ability to accurately predict binding affinity and other bioactivity values to distinguish between potential drug-target interactions. The proposed model, named the Augmented Deep Meta Ensemble for Drug-Target Interaction (ADME-DTI), leverages multiple descriptors and fingerprint representations to extract meaningful insights from drug and protein data. These submodels from each representation are then combined with a deep learning architecture along with the metadata of the drug and target entries. The proposed approach has proved competitive performance against state-of-the-art models across diverse datasets and evaluation metrics, as evidenced by key metrics, namely <math> <semantics> <mrow><msubsup><mi>r</mi> <mi>m</mi> <mn>2</mn></msubsup> </mrow> <annotation>$r_m^2$</annotation></semantics> </math> , concordance index, and mean squared error. Specifically, the model achieved mean squared error values of 0.186 (Davis), 0.118 (Kiba), 0.134 (DTC), 0.262 (Metz), 0.300 (ToxCast), and 0.791 (STITCH), all of which are publicly available benchmark datasets commonly used in drug-target interaction prediction tasks. The results highlight the model's effectiveness in reducing prediction errors and improving accuracy. Previous drug-target interaction research often relied on limited, non-diverse datasets, reducing generalizability. Few studies addressed drug-target interaction prediction as a regression task. Our novel deep learning metamodel integrates multiple models and representations, surpassing benchmarks and delivering greater prediction accuracy across varied datasets and evaluation metrics.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70033"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space.","authors":"Thaís Caroline Gonçalves, Eduardo Henrique Toral Cortez, Danilo T Amaral","doi":"10.1002/minf.70039","DOIUrl":"10.1002/minf.70039","url":null,"abstract":"<p><p>Venom-derived peptides are structurally diverse bioactive scaffolds with growing relevance for computational discovery and molecular design. However, their organization within chemical space remains poorly characterized in integrated frameworks. Here, we present a curated, structure-informed venom peptide atlas supported by an integrated descriptor-based framework combining sequence- and structure-derived features across 3,423 peptides. High-confidence AlphaFold models were integrated with physicochemical and structural descriptors to generate a unified molecular representation. Multivariate analyses reveal that venom peptides occupy constrained and interpretable regions of chemical space shaped by disulfide-mediated stabilization, structural compactness, and electrostatic-hydrophobic balance. Functional classes show clear separation, whereas taxonomic origin explains less variance, supporting convergent design principles. This atlas provides a reference resource for comparative analyses, scaffold prioritization, and hypothesis generation in peptide science.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70039"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148016431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Molecules Generation Using Graph Generative Adversarial Networks.","authors":"Amodini A P, Chandra Mohan Dasari, Santhosh Amilpur","doi":"10.1002/minf.70031","DOIUrl":"10.1002/minf.70031","url":null,"abstract":"<p><p>De novo molecular generation remains a central challenge in drug discovery due to the vastness of chemical space and the difficulty of generating molecules that are simultaneously valid, diverse, and property-aware. While graph-based generative models have improved structural validity over sequence-based approaches, many existing methods suffer from mode collapse, limited controllability, and unstable reinforcement learning dynamics. In this work, we propose NovMolG-GAN, a graph-based generative adversarial framework that integrates graph attention mechanisms, a learnable reward network, mode-seeking regularization, and proximal policy optimization-based reinforcement learning to address these limitations. The proposed model enables stable training while explicitly encouraging molecular validity, novelty, diversity, and drug-likeness. Experiments on the ChEMBL-35 dataset demonstrate that NovMolG-GAN achieves high validity (99.6%), high novelty (99.4%) strong uniqueness (88.5%), and competitive quantitative drug-likeness scores under unconstrained generation. Furthermore, a conditional generation ablation incorporating synthetic accessibility as an additional reward illustrates the controllability of the framework and its ability to steer generation toward synthesis-feasible regions of chemical space. These results highlight the potential of NovMolG-GAN as a flexible and extensible foundation for goal-directed molecular design.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70031"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147868957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChemBang: Expanding the Chemical Space Around Small Molecules.","authors":"Diana Montes-Grajales, Luca Menestrina, Ricard Garcia-Serna, Jordi Mestres","doi":"10.1002/minf.70036","DOIUrl":"10.1002/minf.70036","url":null,"abstract":"<p><p>Efficient exploration of chemical space is an essential component of modern generative drug design. Herein, we introduce ChemBang, a computational engine that grows small molecules based on chemical transformations extracted by matched molecular pair analysis of all structures available in catalogues of synthesized molecules. Each chemical transformation is mapped onto its associated atomic environment defined as the substructure within a three-atom radius from the transformation site. Unsupervised chemical evolution is then performed in cycles by systematically applying chemical transformations to all exposed atomic environments present in a seed structure. Multiple physicochemical properties and substructural alerts are incorporated to effectively guide the generation of drug-like synthetically accessible molecules. As a use case, the generation of the Erdafitinib structure from any of its three ring systems (pyrazole, benzene and quinoxaline), and the evolution of the property distributions from all molecules generated in each cycle, are discussed in detail. The ability to explore the chemical space of pharmaceutical relevance is shown by successfully generating the exact chemical structure of 95.3% of all 2,809 small-molecule ATC drugs from their constituting fragments.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70036"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning.","authors":"Phu Pham","doi":"10.1002/minf.70037","DOIUrl":"10.1002/minf.70037","url":null,"abstract":"<p><p>Learning expressive representations for complex, size-varied molecular graphs remains a fundamental challenge in toxic molecular property prediction and regression. The inherent nonlinearity of atomic interactions, together with intricate structural dependencies and latent high-order structural characteristics, makes accurate graph-based learning particularly difficult. Although recent deep learning (DL)/graph neural network (GNN) approaches have leveraged message passing, attention mechanisms, and graph transformer architectures, these techniques still suffer from limited nonlinear expressiveness, insufficient modeling of high-order structural information, and a lack of explicit uncertainty handling. To address these limitations, we propose a novel AKAGTL model, which is an attention-driven Kolmogorov-Arnold network (KAN)-based graph transformer framework for toxic molecular graph embedding and regression learning. Unlike existing approaches that rely on linear or shallow nonlinear transformations, our proposed AKAGTL model introduces a structured KAN-based functional transformation to explicitly model complex high-order atomic interactions within an attention-driven graph transformer backbone. In addition, high-order structural representations are systematically incorporated to complement structural encoding, while a Gaussian neuro-fuzzy fusion mechanism enables uncertainty-aware aggregation of heterogeneous feature spaces. The proposed framework is evaluated on multiple molecular toxicity benchmarks under a unified experimental protocol with repeated runs. Comprehensive experiments within benchmark molecular graph datasets demonstrate that our AKAGTL model can consistently improve regression accuracy compared to representative GNN and graph transformer baselines. These findings suggest that jointly modeling nonlinear functional interactions, structural dependencies, and uncertainty-aware fusion provides a more expressive and robust solution for toxic molecular graph embedding and regression learning.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70037"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constellation Plots in KNIME: An Automated Scaffold-Based Workflow for Interactive Chemical Space Visualization.","authors":"Carlos D Ramírez-Márquez, Edgar López-López, José L Medina-Franco","doi":"10.1002/minf.70035","DOIUrl":"10.1002/minf.70035","url":null,"abstract":"<p><p>Chemical space analysis is extensively used in different chemistry areas, ranging from the study of natural products to drug discovery projects. Its versatility stems from the ability to integrate continuous properties with molecular representations. This data is used to generate visualizations through dimensionality reduction algorithms. Constellation Plots have been proposed as a general approach to the visual representation of chemical space by encoding structural similarity, scaffold contents, frequency, and continuous properties into a single coordinate-based map. Thus, Constellation Plots provide a high-density visual representation of the chemical space of compound datasets with complex relations. Despite the versatility of Constellation Plots, there remains a significant lack of intuitive, user-friendly, or low-code protocols to automate the generation of these plots for non-computational experts. Herein, we present an interactive and automated scaffold-based Constellation Plot workflow developed within the open-source platform KNIME, facilitating chemical space visualization and analysis. To illustrate the application of the workflow, we used a dataset of 5,211 compounds that inhibit Tau protein, a key therapeutic target for Alzheimer's disease. The KNIME workflow is a general resource that can be used to analyze virtually any data set annotated with a property, including biological activity. The workflow is freely available at: https://github.com/Daniphantom99/KNIME_Constellation_plots.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70035"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO₂ Separation Membranes.","authors":"Yuto Shino, Motosuke Katayama, Yuri Ito, Hiromasa Kaneko","doi":"10.1002/minf.70032","DOIUrl":"10.1002/minf.70032","url":null,"abstract":"<p><p>Membrane technologies for the separation of gases, such as CO₂/CH₄ mixtures, have attracted attention because of their high energy efficiency. Polyimides are considered promising membrane materials for CO₂ separation, and there is a growing demand for materials with even higher performance. In the screening of candidate materials, it is essential to consider not only separation performance but also solubility and polymerizability during the synthesis process. Low solubility or polymerizability can inhibit membrane fabrication and the evaluation of separation performance, potentially leading to wasted resources and effort. In this study, we developed machine learning models to predict the solubility and polymerizability of polyimides. Mixture features derived from molecular descriptors of multiple monomers and mixing ratios were used as inputs for the classification models. The models were then applied to novel candidates, and their effectiveness was validated experimentally.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 4","pages":"e70032"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of DeepQSAR Models for the Discovery of Peptides With Enhanced Antimicrobial and Antibiofilm Potential.","authors":"Jiaying You, Hazem Mslati, Evan F Haney, Noushin Akhoundsadegh, Robert E W Hancock, Artem Cherkasov","doi":"10.1002/minf.70029","DOIUrl":"10.1002/minf.70029","url":null,"abstract":"<p><p>Increasing concerns regarding prolonged antibiotic usage have spurred the search for alternative treatments. Antimicrobial peptides (AMPs), first discovered in the 1980s, have exhibited significant potential against a broad range of bacteria. Short-sequenced AMPs are abundant in nature and present across various organisms. Recently, machine learning technologies such as Quantitative Structure Activity Relationships (QSAR) have enabled expedited discovery of potential AMPs with broad-spectrum antibacterial activity as the amount of available AMP training data increases. Among those, Deep QSAR has recently emerged as a distinct type of application that utilizes conventional molecular descriptors in conjunction with more powerful deep learning (DL) models. Here, we demonstrate the power of Deep QSAR in predicting broad-spectrum AMP activity. Using a recurrent neural network-based QSAR model, we achieved nearly 90% fivefold cross-validated accuracy in classifying AMP activity. Using the developed approach, we designed 98 novel peptides, of which 36 experimentally demonstrated more effective antibiofilm activity and 26 peptides exhibited stronger antimicrobial activity compared to a well-characterized host defense peptide IDR-1018, which was demonstrated to possess broad spectrum antibiofilm activity against a wide range of bacterial pathogens and a previous computer-aided peptide design study employing IDR-1018 derivatives successfully identified novel peptides with enhanced antibiofilm activity. Notably, 22 of those peptides demonstrated improvements of both antimicrobial and, particularly, antibiofilm properties, making them suitable prototypes for preclinical development and demonstrating efficacy of DeepQSAR modeling in identifying novel and potent AMPs.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 4","pages":"e70029"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147699232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current Insights on Skin Permeability Data and Quantitative Structure-Property Relationship Modeling.","authors":"Farah Asgarkhanova, Shamkhal Baybekov, Gilles Marcou, Catherine Champmartin, Lisa Chedik, Frédéric Cosnier, Mikhail Volkov, Louis Plyer, Dragos Horvath, Alexandre Varnek","doi":"10.1002/minf.70030","DOIUrl":"10.1002/minf.70030","url":null,"abstract":"<p><p>Skin permeability is a critical factor in pharmaceuticals, cosmetics, and occupational safety. Experimental determination of skin permeability coefficients (K_p) is time-intensive and resource-intensive, highlighting the importance of computational predictions. This study presents a quantitative structure-property relationship (QSPR) model developed using the recently published SkinPiX dataset and the HuskinDB skin permeability database, comprising 209 curated compounds with associated K_p values and metadata. The model performance was assessed on three new experimental data points. The datasets and models are freely available, providing a valuable tool to enhance decision-making and support the development of safer and more effective products.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 4","pages":"e70030"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}