Search Results (236)

Dual-mode fluorescent materials are vital in bioimaging, sensing, displays, and lighting, owing to their efficient emission of visible or near-infrared light. Traditional optimization methods, including empirical experiments and quantum chemical computations, suffer from high costs, high labor intensities, and difficulties capturing complex relationships among molecular structures, synthesis parameters, and key photophysical properties. In this review, fundamental principles, key methodologies, and representative applications of machine learning (ML) in predicting fluorescent material performance are systematically summarized. The core ML techniques covered include supervised regression, neural networks, and physics-informed hybrid frameworks. The representative fluorescent materials analyzed encompass aggregation-induced emission (AIE) luminogens, thermally activated delayed fluorescence (TADF) emitters, quantum dots, carbon dots, perovskites, and inorganic phosphors. This review details the modeling approaches and typical workflows—such as data preprocessing, descriptor selection, and model validation—and highlights algorithmic optimization strategies such as data augmentation, physical constraints embedding, and transfer learning. Finally, prevailing challenges, including limited high-quality data availability, weak model interpretability, and insufficient model transferability, are discussed. Full article

The practice of using optimal descriptors has been applied for more than twenty years to develop in silico models. In the present study, a series of in silico models was built to predict the acute fish toxicity of pharmaceuticals using optimal descriptors. The SMILES format was used to represent the chemical structure. The data were split into five training and validation sets. The obtained model for fish toxicity yielded a determination coefficient of 0.67 for the external validation set, representing an acceptable quality, considering the complexity of the pharmaceuticals given their molecular structure and specific biological activity. This study is useful for assessing the acute fish toxicity of pharmaceuticals and, in general terms, as an approach to building models for complex biological endpoints. Full article

Thyroid hormone (TH) system disruption by chemicals poses a significant concern due to the key role the TH system plays in essential body functions, including the metabolism, growth, and brain development. Animal-based testing methods are resource-demanding and raise ethical issues. Thus, there is a recognised need for new approach methodologies, such as quantitative structure–activity relationship (QSAR) models, to advance chemical hazard assessments. This review, covering the scientific literature from 2010 to 2024, aimed to map the current landscape of QSAR model development for predicting TH system disruption. The focus was placed on QSARs that address molecular initiating events within the adverse outcome pathway for TH system disruption. A total of thirty papers presenting eighty-six different QSARs were selected based on predefined criteria. A discussion on the endpoints and chemical classes modelled, data sources, modelling approaches, and the molecular descriptors selected, including their mechanistic interpretations, was provided. By serving as a “state-of-the-art” of the field, existing models and gaps were identified and highlighted. This review can be used to inform future research studies aimed at advancing the assessment of TH system disruption by chemicals without relying on animal-based testing, highlighting areas that require additional research. Full article

Resveratrol, a natural stilbene found in various plants, is mainly known for its strong antioxidant activities exhibiting a comprehensive range of treatments for some skin disorders such as skin cancer, photoaging, dermatitis, and melanogenesis. However, few studies have been conducted on the differences in biological activities between resveratrol and its derivatives. Therefore, we aimed to investigate the effects of resveratrol (Re) and its derivatives acetyl-resveratrol (Are), cis-trismethoxy resveratrol (Cre), dihydroresveratrol (Dre), and oxyresveratrol (Ore) on antioxidant and anti-tyrosinase effects using in vitro and in silico methods. In the in vitro results, Ore showed the highest antioxidant activity among the resveratrol derivatives and displayed stronger inhibitory activity against natural tyrosinase compared with that of kojic acid. Density functional theory (DFT) was used to calculate quantum chemical descriptors to understand the compounds’ electronic and physicochemical properties. Molecular docking and molecular dynamics simulations were also performed to explore the corresponding binding mode and structural behavior, revealing that Ore exhibited the strongest binding interactions among resveratrol derivatives, primarily through hydrogen bonds and hydrophobic interactions with key amino acid residues. Moreover, all the resveratrol compounds demonstrated drug-likeness properties with predicted safe skin toxicity profiles. In conclusion, Ore exhibited the strongest tyrosinase inhibition and antioxidant activity among resveratrol derivatives in both in vitro and in silico assessments. Further research on the development of medicines, cosmetics, and food supplements of such compounds should be conducted. Full article

A new Schiff base, (E)-2-(((1,10-phenanthrolin-5-yl)imino)methyl)-4,6-di-tert-butylphenol (Fen-IHB), was designed to incorporate an intramolecular hydrogen bond (IHB) between the phenolic OH and the azomethine nitrogen with the goal of modulating its physicochemical and biological properties. Fen-IHB was synthesized by condensation of 5-amino-1,10-phenanthroline with 3,5-di-tert-butyl-2-hydroxybenzaldehyde and exhaustively characterized by HR-ESI-MS, FTIR, 1D/2D NMR (¹H, ¹³C, DEPT-45, HH-COSY, CH-COSY, D₂O exchange), and UV–Vis spectroscopy. Cyclic voltammetry in anhydrous CH₃CN revealed a single irreversible cathodic peak at −1.43 V (vs. Ag/Ag⁺), which is consistent with the intramolecular reductive coupling of the azomethine moiety. Density functional theory (DFT) calculations, including MEP mapping, Fukui functions, dual descriptor analysis, and Fukui potentials with dual descriptor potential, identified the exocyclic azomethine carbon as the principal nucleophilic site and the phenolic ring (hydroxyl oxygen and adjacent carbons) as the main electrophilic region. Noncovalent interaction (NCI) analysis further confirmed the strength and geometry of the intramolecular hydrogen bond (IHB). In vitro antimicrobial assays indicated that Fen-IHB was inactive against Gram-negative facultative anaerobes (Salmonella enterica serovar Typhimurium and Typhi, Escherichia coli) and strictly anaerobic Gram-positive species (Clostridioides difficile, Roseburia inulinivorans, Blautia coccoides), as any growth inhibition was indistinguishable from the DMSO control. Conversely, Fen-IHB displayed measurable activity against Gram-positive aerobes and aerotolerant anaerobes, including Bacillus subtilis, Streptococcus pyogenes, Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus haemolyticus. Overall, these comprehensive characterization results confirm the distinctive chemical and electronic properties of Fen-IHB, underlining the crucial role of the intramolecular hydrogen bond and electronic descriptors in defining its reactivity profile and selective biological activity. Full article

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm³, Soman: 1.03 g/cm³) and A-agents (e.g., A230: 1.608 g/cm³, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD₅₀). Predicted LD₅₀ values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD₅₀ = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation. Full article

Phytoecdysteroids represent a class of naturally occurring substances known for their diverse biological functions, particularly their strong ability to stimulate protein anabolism. In this study, a computational machine learning-driven quantitative structure–activity relationship (QSAR) approach was applied to analyze the anabolic potential of 23 ecdysteroid compounds. The ML-based QSAR modeling was conducted using a combined approach that integrates Genetic Algorithm-based feature selection with Multiple Linear Regression Analysis (GA-MLRA). Additionally, structure optimization by semi-empirical quantum-chemical method was employed to determine the most stable molecular conformations and to calculate an additional set of structural and electronic descriptors. The most effective QSAR models for describing the anabolic activity of the investigated ecdysteroids were developed and validated. The proposed best model demonstrates both strong statistical relevance and high predictive performance. The predictive performance of the resulting models was confirmed by an external test set based on R²_test values, which were within the range of 0.89 to 0.97. Full article

Resistance of various bacterial pathogens to the activity of clinically approved drugs currently leads to serious infections, rapid spread of difficult-to-treat diseases, and even death. Taking the threats for human health in mind, researchers are focused on the isolation and characterization of novel natural products, including plant secondary metabolites. These molecules serve as inspiration and a suitable structural platform in the design and development of novel semi-synthetic and synthetic derivatives. All considered compounds have to be adequately evaluated in silico, in vitro, and in vivo using relevant approaches. The current review paper briefly focuses on the chemical and metabolic properties of resveratrol (1), as well as its oligomeric structures, viniferins, and viniferin-based molecules. The core scaffolds of these compounds contain so-called privileged structures, which are also present in many clinically approved drugs, indicating that those natural, properly substituted semi-synthetic, and synthetic molecules can provide a notably broad spectrum of beneficial pharmacological activities, including very impressive antimicrobial efficiency. Except for spectral verification of their structures, these compounds suffer from the determination or prediction of other structural and physicochemical characteristics. Therefore, the structure–activity relationships for specific dihydrodimeric and dimeric viniferins, their bioisosteres, and derivatives with notable efficacy in vitro, especially against chosen Gram-positive bacterial strains, are summarized. In addition, a set of descriptors related to their structural, physicochemical, pharmacokinetic, and toxicological properties is generated using various computational tools. The obtained values are compared to those of clinically approved drugs. The particular relationships between these in silico parameters are also explored. Full article

This study focuses on exploring potential inhibitors of the Marburg virus interferon inhibitory domain protein (MARV-VP35), which is responsible for immune evasion and immunosuppression during viral manifestation. A combination of in silico techniques was applied, including structure-based pharmacophore virtual screening, molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, molecular dynamics (MD), and molecular stability assessment of the identified hits. The docking scores of the 14 selected ligands ranged between −6.88 kcal/mol and −5.28 kcal/mol, the latter being comparable to the control ligand. ADMET and drug likeness evaluation identified Mol_01 and Mol_09 as the most promising candidates, both demonstrating good predicted antiviral activity against viral targets. Density functional theory (DFT) calculations, along with relevant quantum chemical descriptors, correlated well with the docking score hierarchy, and molecular electrostatic potential (MEP) mapping confirmed favorable electronic distributions supporting the docking orientation. Molecular dynamics simulations further validated complex stability, with consistent root mean square deviation (RMSD), root mean square fluctuation (RMSF), and secondary structure element (SSE) profiles. These findings support Mol_01 and Mol_09 as viable candidates for experimental validation. Full article

Four new minor monosulfated triterpene penta- and hexaosides, cladolosides S (1), S₁ (2), T (3), and T₁ (4), were isolated from the Vietnamese sea cucumber Cladolabes schmeltzii (Sclerodactylidae, Dendrochirotida). The structures of the compounds were established based on extensive analysis of 1D and 2D NMR spectra as well as HR-ESI-MS data. Cladodosides S (1), S₁ (2) and T (3), T₁ (4) are two pairs of dehydrogenated/hydrogenated compounds that share identical carbohydrate chains. The oligosaccharide chain of cladolosides of the group S is new for the sea cucumber glycosides due to the presence of xylose residue attached to C-4 Xyl1 in combination with a sulfate group at C-6 MeGlc4. The oligosaccharide moiety of cladolosides of the group T is unique because of the position of the sulfate group at C-3 of the terminal sugar residue instead of the 3-O-Me group. This suggests that the enzymatic processes of sulfation and O-methylation that occur during the biosynthesis of glycosides can compete with each other. This can presumably occur due to the high level of expression or activity of the enzymes that biosynthesize glycosides. The mosaicism of glycoside biosynthesis (time shifting or dropping out of some biosynthetic stages) may indicate a lack of compartmentalization inside the cells of organism producers, leading to a certain degree of randomness in enzymatic reactions; however, this also offers the advantage of providing chemical diversity of the glycosides. Analysis of the hemolytic activity of a series of 26 glycosides from C. schmeltzii revealed some patterns of structure–activity relationships: the presence or absence of 3-O-methyl groups has no significant impact, hexaosides, which are the final products of biosynthesis and predominant compounds of the glycosidic fraction of C. schmeltzii, are more active than their precursors, pentaosides, and the minor tetraosides, cladolosides of the group A, are weak membranolytics and therefore are not synthesized in large quantities. Two glycosides from C. schmeltzii, cladolosides D (18) and H₁ (26), display selectivity of cytotoxic action toward triple-negative breast cancer cells MDA-MB-231, while remaining non-toxic in relation to normal mammary cells MCF-10A. Quantitative structure–activity relationships (QSAR) were calculated based on the correlational analysis of the physicochemical properties and structural features of the glycosides and their hemolytic and cytotoxic activities against healthy MCF-10A cells and cancer MCF-7 and MDA-MB-231 cell lines. QSAR highlighted the complexity of the relationships as the cumulative effect of many minor contributions from individual descriptors can have a significant impact. Furthermore, many structural elements were found to have different effects on the activity of the glycosides against different cell lines. The opposing effects were especially pronounced in relation to hormone-dependent breast cancer cells MCF-7 and triple-negative MDA-MB-231 cells. Full article

Type 2 diabetes mellitus (T2DM) is a complex and prevalent metabolic disorder, and dipeptidyl peptidase 4 (DPP4) inhibitors have proven effective, yet the identification of novel inhibitors remains challenging due to the vastness of chemical space. In this study, we developed DPPPRED-IV, a web-based ensembled system integrating both binary classification and continuous regression Quantitative Structure Activity Relationships (QSAR) models to predict human DPP4 inhibitory activity. A curated dataset of 4 676 ChEMBL compounds was subjected to genetic algorithm descriptor selection and multiple machine learning algorithms; classification models were combined via a soft voting ensemble, while regression models estimated IC₅₀ values. All models underwent external 10-fold cross-validation and applicability domain analysis. The final models were integrated into a user-friendly web server, allowing predictions from SMILES inputs. Experimental testing of 29 MolPort compounds at 1.5 µM confirmed that 14 predicted actives exhibited significant inhibition, supporting the tool’s performance in early-stage screening. DPPPRED IV is freely available within the ChemoPredictionSuite and offers a resource to accelerate decision making, reduce costs and minimize animal use in T2DM drug discovery. Full article

The expanding field of nutraceuticals and functional food science is increasingly turning to marine-derived bioactive compounds, particularly saponins, for their diverse pharmacological properties. These so-called thalassochemicals display distinctive structural features—such as sulfated glycosidic moieties and amphiphilic backbones—that underpin potent antitumor, hypolipidemic, antioxidant, and antimicrobial activities. In contrast to their terrestrial analogs, marine saponins remain underexplored, and their complexity poses analytical and functional challenges. This review provides a critical and integrative synthesis of recent advances in the structural elucidation, biological function, and technological application of marine saponins. Special emphasis is placed on the unresolved limitations in their isolation, characterization, and structural validation, including coelution of isomers, adduct formation in MS spectra, and lack of orthogonal techniques such as NMR or FTIR. We illustrate these limitations through original MS/MS data and propose experimental workflows to improve compound purity and identification fidelity. In addition to discussing known structure–activity relationships (SARs) and mechanisms of action, we extend the scope by integrating recent developments in computational modeling, including machine learning, molecular descriptors, and quantitative structure–activity relationship (QSAR) models. These tools offer new avenues for predicting saponin bioactivity, despite current limitations in available high-quality datasets. Furthermore, we include a classification and comparison of steroidal and triterpenoid saponins from marine versus terrestrial sources, complemented by detailed chemical schematics. We also address the impact of processing techniques, delivery systems, and bioavailability enhancements using encapsulation and nanocarriers. Finally, this review contextualizes these findings within the regulatory and sustainability frameworks that shape the future of saponin commercialization. By bridging analytical chemistry, computational biology, and food technology, this work establishes a roadmap for the targeted development of marine saponins as next-generation nutraceuticals and functional food ingredients. Full article

Background/Objective: This study aimed to develop a predictive model to classify and rank highly active compounds that inhibit HIV-1 integrase (IN). Methods: A total of 2271 potential HIV-1 inhibitors were selected from the ChEMBL database. The most relevant molecular descriptors were identified using a hybrid GA-SVM-RFE approach. Predictive models were built using Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The models underwent a comprehensive evaluation employing calibration, Y-randomization, and Net Gain methodologies. Results: The four models demonstrated intense calibration, achieving an accuracy greater than 0.88 and an area under the curve (AUC) exceeding 0.90. Net Gain at a high probability threshold indicates that the models are both effective and highly selective, ensuring more reliable predictions with greater confidence. Additionally, we combine the predictions of multiple individual models by using majority voting to determine the final prediction for each compound. The Rank Score (weighted sum) serves as a confidence indicator for the consensus prediction, with the majority of highly active compounds identified through high scores in both the 2D descriptors and ECFP4-based models, highlighting the models’ effectiveness in predicting potent inhibitors. Furthermore, cluster analysis identified significant classes associated with vigorous biological activity. Conclusions: Some clusters were found to be enriched in highly potent compounds while maintaining moderate scaffold diversity, making them promising candidates for exploring unique chemical spaces and identifying novel lead compounds. Overall, this study provides valuable insights into predicting integrase binders, thereby enhancing the accuracy of predictive models. Full article

Kapakahines A–G are natural products isolated from the marine sponge Carteriospongia sp., characterized by complex molecular architectures composed of fused rings and diverse functional groups. Preliminary studies have indicated that some of these peptides may exhibit cytotoxic and antitumor activities, which has prompted interest in further exploring their chemical and pharmacokinetic properties. Computational chemistry—particularly Conceptual Density Functional Theory (CDFT)-based Computational Peptidology (CP)—offers a valuable framework for investigating such compounds. In this study, the CDFT-CP approach is applied to analyze the structural and electronic properties of Kapakahines A–G. Alongside the calculation of global and local reactivity descriptors, predicted ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles and pharmacokinetic parameters, including pKa and LogP, are evaluated. The integrated computational analysis provides insights into the stability, reactivity, and potential drug-like behavior of these marine-derived cyclopeptides and contributes to the theoretical groundwork for future studies aimed at optimizing their bioactivity and safety profiles. Full article

In today’s information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance of informatics in shaping the field of medicinal chemistry, particularly through the concept of the “informacophore”. The informacophore refers to the minimal chemical structure, combined with computed molecular descriptors, fingerprints, and machine-learned representations of its structure, that are essential for a molecule to exhibit biological activity. Similar to a skeleton key unlocking multiple locks, the informacophore points to the molecular features that trigger biological responses. By identifying and optimizing informacophores through in-depth analysis of ultra-large datasets of potential lead compounds and automating standard parts in the development process, there will be a significant reduction in biased intuitive decisions, which may lead to systemic errors and a parallel acceleration of drug discovery processes. Full article