Search Results (48)

Direct human evidence linking Scrophularia ningpoensis to asthma is limited, and database-derived herb–target records do not establish constituent exposure or target engagement. We used an evidence-audited molecular-informatics workflow to prioritize asthma airway-brushing-associated genes overlapping a frozen HERB export. GSE63142 was the discovery set, GSE67472 the independent airway-epithelial replication set, GSE137268 an induced-sputum cross-biospecimen transfer set, and GSE43696 a descriptive same-cohort reference because 105 of 108 identifiers overlapped GSE63142. Of 1266 measurable HERB-annotated targets, an 81-gene FDR-defined overlap did not exceed an expression-matched null (p = 0.0972), whereas a 19-gene strict overlap showed 2.27-fold enrichment (empirical p = 3.00 × 10⁻⁴) and 12 genes met the prespecified replication criterion in GSE67472. Provenance sensitivity retained two genes after database-mining-only edges were excluded and none under a direct-target-engagement requirement. Nested classifiers transferred to GSE67472, but not to sputum. Out-of-fold SHAP identified model-specific contributors, with moderate cross-cohort rank stability for LASSO (Spearman ρ = 0.563) and random forest (ρ = 0.693); SHAP was not interpreted as causal or herbal-target importance. These findings support computational prioritization and transferability assessment, not chemical presence, exposure, target binding, mechanism, or efficacy. Full article

In the context of artificial intelligence in chemistry, the first applications that come to mind are widely discussed ones, such as building surrogate models to replace costly numerical simulations or designing new compounds. However, one area where generative AI truly stands out is programming assistance, ranging from code improvement suggestions to vibe coding simple programs from scratch. This article explores how these tools can be used by chemists to test new ideas, make the most of existing open-source software and rapidly develop custom solutions that significantly ease day-to-day work. Using the problem of estimating Hansen solubility parameters as an academic example, their strengths and limitations are illustrated through fully customizable sample codes, including a solubility parameter calculator, a molecular spreadsheet library and a molecular editor. In addition to serving as models, these codes could be of practical interest to some readers due to unique features making them address gaps in the current offerings. Full article

Bioactive peptides constitute a highly diverse and therapeutically relevant molecular class, yet their systematic exploration remains challenging because of the vast size, heterogeneity, and fragmented annotation of peptide chemical space. In this context, complex networks have emerged as a complementary computational framework for organizing, analyzing, and exploiting peptide diversity. This methodological review examines the main components of graph-based peptide informatics, from graph-based data integration and curated repositories to descriptor-based representations, similarity-driven network construction, and topology-informed analysis. We describe how peptide sequences can be projected into multidimensional reference spaces using molecular descriptors, aggregation operators, and unsupervised feature selection, and how these representations support the construction of Chemical Space Networks, Half-Space Proximal Networks, and Metadata Networks. Special attention is given to topological analysis, including threshold selection, community detection, and centrality-based identification of representative peptides and scaffolds. We also review the development of Multi-query Similarity Searching Models as training-independent, topology-guided alternatives to conventional supervised predictors. Finally, we highlight the implementation of these methodologies in computational resources such as StarPepDB, StarPep Toolbox, and StarPepWeb, which illustrate the transition of peptide network science from conceptual workflows to accessible, scalable, and reproducible infrastructures. Overall, complex networks are presented as a mature and interpretable paradigm for the structured exploration, analysis, and discovery of bioactive peptides. Full article

Chemical artificial intelligence increasingly relies on molecular property prediction models trained from proprietary compound libraries, bioassay records, and reaction-screening data. However, these data often contain commercially sensitive structures, confidential activity labels, and privacy-relevant experimental metadata, making direct centralization impractical. This paper proposes PrivMol, a privacy-preserving computational chemistry framework for federated molecular representation learning. PrivMol introduces two novel algorithms: Secure Substructure-Aware Federated Optimization and Differentially Private Molecular Gradient Calibration. The first algorithm decomposes molecular graphs into privacy-sensitive and task-relevant substructure regions, enabling local clients to train graph neural networks while transmitting only securely aggregated model updates. The second algorithm adaptively calibrates clipping and perturbation according to atom- and substructure-level contribution scores, reducing unnecessary utility loss on chemically informative fragments while retaining formal differential privacy guarantees. To improve robustness under heterogeneous chemical spaces, PrivMol incorporates local contrastive molecular alignment without exposing raw molecules, labels, scaffolds, substructure masks, or embeddings. Experimental evaluation on widely used public molecular benchmarks, including ESOL, FreeSolv, Lipophilicity, BBBP, BACE, HIV, and Tox21, demonstrates that PrivMol provides a favorable trade-off among prediction accuracy, communication efficiency, empirical leakage resistance, and privacy protection. The study offers a practical route toward secure collaborative chemical intelligence for computer-aided drug discovery, toxicology prediction, and materials informatics. Full article

Surface engineering strongly influences the performance, reliability, and safety of medical and biomedical devices, yet failures often originate at interfaces rather than in bulk materials alone. This review addresses the fragmented evidence base linking coating selection, interphase design, qualification testing, advanced characterization, and data-driven durability analysis. The objective is to provide an integrative, failure-mode-based framework for implants, reusable instruments, inhalation systems, diagnostics, wearables, and implantable electronics. A narrative synthesis of the peer-reviewed literature in coatings, biomaterials, electrochemistry, reliability, standards, and materials informatics was conducted, with qualitative tables used only when protocols were too heterogeneous for numerical pooling. The review compares physical vapor deposition (PVD), chemical and plasma-enhanced chemical vapor deposition (CVD/PECVD), atomic layer deposition (ALD), sol–gel/organically modified silica (ORMOSIL) hybrids, plasma polymers, parylene, bioactive or antimicrobial surfaces, and electronic encapsulation strategies. The main finding is that no universally superior coating exists; reliable performance depends on matching architecture and characterization to the dominant failure pathway, substrate compliance, geometry, sterilization or physiologic exposure, and the standards-constrained endpoint. The review further shows how electrochemical diagnostics, interfacial mechanics, multiphysics models, survival/reliability statistics, and carefully governed AI workflows can be combined to support service-life prediction and decision-oriented qualification. Full article

This study presents TCM-MS2Link, a standardized mass spectrometry-based association dataset for traditional Chinese medicine (TCM), serving as an important resource for natural product research in TCM. The dataset adopts a dual-layer “knowledge–data” architecture: the first layer, TCM-MolLink, comprises curated herb–compound association data, constructed through the integration of multiple heterogeneous databases and rigorous consistency filtering to establish high-confidence relationships between TCM herbs and their chemical constituents; the second layer, MS2-MLReady, is a benchmark dataset for mass spectrometry-based machine learning which, after systematic data cleaning, standardized preprocessing, and well-designed data partitioning, can directly support the training and evaluation of artificial intelligence models. By addressing key limitations in existing public resources, including data fragmentation, inconsistent annotations, and insufficient computational usability, TCM-MS2Link effectively overcomes major bottlenecks in the systematic analysis of TCM components and data-driven research. This study significantly enhances the reliability of herb–compound associations and the modeling readiness of mass spectrometry data, providing a high-quality, standardized, and reusable data foundation for applications such as TCM knowledge base construction and automated spectrum–structure identification, thereby promoting the advancement of TCM informatics and data-driven research. Full article

Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(p-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (∼95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries. Beyond identifying potential replacements, this work establishes a generalizable framework for navigating vast polymer design spaces under competing performance constraints. The results illustrate how data-driven polymer design can bridge the gap between sustainability concepts and experimentally realizable materials for real-world packaging applications. Full article

Background: The visceral organs of sea cucumbers belonging to the family Stichopodidae, also known as Stichopodidae Viscus (SV), have been traditionally used for the management of gastrointestinal disorders. Experimental evidence has shown that the ethanol extract of SV (SVE) alleviates ulcerative colitis (UC) symptoms in a mouse model. However, the chemical constituents of SVE and the potential molecular targets mediating its effects in UC remain unclear. Methods: In this study, SVE was prepared from Apostichopus japonicus (Selenka). A reliable and sensitive strategy integrating advanced analytical and informatics tools was employed to profile the chemical components of SVE. Analyses were performed using ultra-performance liquid chromatography coupled with ion mobility spectrometry and quadrupole time-of-flight mass spectrometry operating in high-definition MS^E (UPLC-IMS-Q-TOF-HDMS^E), with data processed using the UNIFI scientific information system. Constituent identification relied on retention time (RT), accurate mass (MS1), experimentally acquired HDMS^E (MS2) spectra, and collision cross-section (CCS). Metabolomics-based approaches were further applied to characterize the in vivo exposure profile of SVE components in mouse serum and colon tissue after oral administration. Subsequently, the putative bioactive constituents and their underlying mechanisms of action were investigated using network pharmacology and molecular docking. Results: Based on the integrated identification strategy, a total of 78 compounds, including saponins, phenolic acids, fatty acids, and amino acids, were annotated in SVE, among which 6 compounds were verified using authentic reference standards to ensure unambiguous identification. Subsequently, 35 features in serum and 24 in the colon were found to be significantly altered following a single oral dose of SVE in mice, and were defined as SVE-related differential constituents. After network pharmacology analyses, 129 shared targets were identified between potential targets of SVE-related components in serum and UC-related targets, including PIK3CA, EGFR, and AKT1. Functional enrichment analysis suggested that SVE might exert its effects in UC through modulation of key nodes within the PI3K-Akt and EGFR signaling pathways, as well as lipid- and atherosclerosis-related pathways. Molecular docking results further indicated moderate binding affinities of representative SVE-related differential components toward PIK3CA, AKT1, and EGFR. Conclusions: This study clarifies the chemical basis and potential UC-related mechanisms of SVE, providing a scientific rationale for the development of SV-derived therapeutic candidates for UC. Full article

Aquatic ecological issues have garnered significant attention in recent years, driving the demand for convenient, effective, and systematic assessment methods in environmental risk evaluation. This review provides a comprehensive introduction to methodologies for assessing the toxicity of chemicals toward aquatic microorganisms, which include viruses, bacteria, fungi, protozoa, and algae. Among these, microalgae are commonly used as model organisms due to their relative simplicity. The article details conventional biological methods, general chemical techniques, modern instrumental analyses, and informatics approaches, with a particular focus on algae and bacteria as model organisms for toxicity assessment. The principles, advantages, and limitations of each method are discussed, along with examples of their application in various contexts. Biological methods offer direct visualization, convenience, and rapid results, while modern instrumental techniques enable mechanistic insights at molecular and biochemical levels. Informatics methods facilitate toxicity evaluation in complex systems. While aquatic microorganisms encompass viruses, fungi, protozoa, bacteria, and algae, this review primarily focuses on bacteria and algae as model organisms due to their ecological relevance, sensitivity, and widespread use in standardized assays. Full article

Nominally weakly coordinating anions are useful for modulating the solubility and chemical properties of metal complexes, but identification and analysis of the systematics of the interactions of anions with cationic metal complexes has not received the attention it deserves. Here, a chemical informatics approach is demonstrated for identifying and quantitatively analyzing the ways that the bis(trifluoromethylsulfonyl)imide anion (TFSI) can interact with metal-containing species. An open access computer program (PyCIFTer) was developed to facilitate large-scale structural analysis of TFSI-containing species by utilization of experimental atomic coordinate data from single-crystal X-ray diffraction (XRD) studies obtained from the Cambridge Structural Database (CSD). PyCIFTer establishes a three-dimensional vector space from the raw atomic coordinates, generating acyclic, undirected graphs that are used to rapidly analyze the structural properties (bond lengths and angles) of TFSI in individual structures in sequential/batch fashion. The structures are sorted by PyCIFTer into groups based on pre-set and chemically sensible criteria, affording a comprehensive and systematic view of TFSI structural chemistry. This approach avoids tedious one-at-a-time interrogation of structures, a prospect unreasonable in this case, and many others of contemporary chemical relevance; there were over 1500 structures in the CSD containing TFSI as of November 2024. The results demonstrate that TFSI only rarely binds to cations in the solid state, favoring the formation of species in which TFSI is found in cations’ outer coordination spheres. The prospect of applying PyCIFTer to other moieties is also discussed. PyCIFTer is also schematically compared to the commercial CSD Python application programming interface (API). Taken together, this work demonstrates the usefulness of modular workflows for sequential/batch analysis of structural data from XRD, an approach that appears poised to accelerate the translation of legacy structural results into new chemical insights and hypotheses. Full article

With the advent of new analytical technologies and the urgent environmental problem, reopening investigations into polluting waste matrices becomes a priority. Olive mill wastewater is a pollutant and phytotoxic by-product of olive oil production. An untargeted UHPLC-QTOF analysis of three olive mill wastewaters from three different olive cultivars was performed, and modern informatic platforms were involved to characterize the chemical components in-depth. Data elaboration and statistical analysis confirmed the differences between samples and revealed a total of 364 annotated compounds, including iridoids, phenolic compounds, flavonoids, lignans, cinnamic acid derivatives, and pyrrolidine derivatives. Many of these metabolites, including compounds with known antioxidant and bioactive potential, are scarcely reported in olive products and by-products. The outcomes of this work could be useful for rethinking olive mill wastewater as a source of bioactive compounds to develop and optimize new detoxification strategies. Full article

Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced material property prediction, integrating textual (chemical compositions), tabular (structural descriptors), and image-based (2D crystal structure visualizations) modalities. Utilizing the Alexandriadatabase, we construct a comprehensive multimodal dataset of 10,000 materials with symmetry-resolved crystallographic data. Specialized neural architectures, such as FT-Transformer for tabular data, Hugging Face Electra-based model for text, and TIMM-based MetaFormer for images, generate modality-specific embeddings, fused through a hybrid strategy into a unified latent space. The framework predicts seven critical material properties, including electronic (band gap, density of states), thermodynamic (formation energy, energy above hull, total energy), magnetic (magnetic moment per volume), and volumetric (volume per atom) features, many governed by crystallographic symmetry. Experimental results demonstrated that multimodal fusion significantly outperforms unimodal baselines. Notably, the bimodal integration of image and text data showed significant gains, reducing the Mean Absolute Error for band gap by approximately 22.7% and for volume per atom by 22.4% compared to the average unimodal models. This combination also achieved a 28.4% reduction in Root Mean Squared Error for formation energy. The full trimodal model (tabular + images + text) yielded competitive, and in several cases the lowest, error metrics, particularly for band gap, magnetic moment per volume and density of states per atom, confirming the value of integrating all three modalities. This scalable, modular framework advances materials informatics, offering a powerful tool for data-driven materials discovery and design. Full article

Molecular odor prediction represents a fundamental challenge in computational chemistry with significant applications in fragrance design, food science, and chemical safety assessment. While traditional Quantitative Structure–Activity Relationship (QSAR) methods rely on hand-crafted molecular descriptors, recent advances in graph neural networks (GNNs) enable direct end-to-end learning from molecular graph structures. However, systematic comparison between these approaches for multi-label odor prediction remains limited. This study presents a comprehensive evaluation of traditional QSAR methods compared with modern GNN approaches for multi-label molecular odor prediction. Using the GoodScent dataset containing 3304 molecules with six high-frequency odor types (fruity, green, sweet, floral, woody, herbal), we systematically evaluate 23 model configurations across traditional machine learning algorithms (Random Forest, SVM, GBDT, MLP, XGBoost, LightGBM) with three feature-processing strategies and three GNN architectures (GCN, GAT, NNConv). The results demonstrate that GNN models achieve significantly superior performance, with GCN achieving the highest macro F1-score of 0.5193 compared to 0.4766 for the best traditional method (MLP with basic preprocessing), representing a 24.1% relative improvement. Critically, we discover that threshold optimization is essential for multi-label chemical classification. These findings establish GNNs as the preferred approach for molecular property prediction tasks and provide crucial insights for handling class imbalance in chemical informatics applications. Full article

A series of novel derivatives of 3-amino-4-hydroxybenzenesulfonamide was synthesized. As the analyzed compounds possess a sulfonamide group, the affinity of these compounds for human carbonic anhydrases (CAs) was measured by fluorescent thermal shift assay, and compound selectivity for different isoenzymes was identified. The crystal structures of the complexes of compound 25 with CAI and CAII were determined. Additionally, the activity of compounds on the viability of three cancer cell lines—human glioblastoma U-87, triple-negative breast cancer MDA-MB-231, and prostate adenocarcinoma PPC-1—was established using the MTT assay and compared to CAIX-selective and non-selective comparative compounds U-104 and acetazolamide. The half-maximal concentration (EC₅₀) was determined for the identified most active compounds, and their selectivity over fibroblasts was established. Compound 9 (inhibitor of multi-CAs) and compound 21 (not binding to CAs), considered the most promising candidates, were tested in cancer cell 3D cultures (cancer spheroids) by assessing their effect on spheroid growth and viability. Both compounds reduced the viability of spheroids from all cancer cell lines. U-87 and PPC-1 spheroids became looser in the presence of compound 9, while the growth of MDA-MB-231 spheroids was slower compared to the control. Compound 21 reduced the growth of U-87 and MDA-MB-231 3D cultures, with no significant effect on PPC-1 spheroids. Full article

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques. Full article