Search Results (1,194)

Ebola virus disease remains one of the most serious viral infections with no approved small-molecule treatments. The Ebola virus glycoprotein (EBOV-GP), which enables the virus’s entry to host cells, is a promising target for drug discovery. In this study, a multistage computer-aided drug discovery approach was used to identify new specific EBOV-GP inhibitors. A reliable QSAR model was built using 55 terpenoid derivatives. This model was able to predict the activity of newly designed compounds with good accuracy and validated statistical metrics ($R_{tr}^2$= 0.70; $R_{ext}^2$ = 0.73). It was subsequently applied to screen over 15,500 newly generated compounds from three lead molecules by fragment-based design tools. Predicted activity, binding affinity toward EBOV-GP, and good ADMET drug-like properties prioritized the eleven most promising hits. Through 150 ns molecular dynamics simulations, these compounds remained stable in the EBOV-GP binding site. Further binding free energy analysis (MM/PBSA) showed strong binding affinities, especially for the compounds L-60, L-832, M-1618, and L-1366. This study showed how combining QSAR, fragment-based design, docking, ADMET, and molecular dynamics could help in identifying potent and safe small molecules against the EBOV-GP. The top compounds are ready for further experimental and in vitro biological testing. Full article

In order to find green fungicides derived from natural products, 22 unreported anethole-based thiazolinone-hydrazone compounds were designed and synthesized, and their structures were characterized by FT-IR, ¹H NMR, ¹³C NMR, and HRMS. At a concentration of 50 mg/L, the preliminary antifungal activity of the target compounds against eight plant pathogens was evaluated. The results showed that 5q (R = m-OH C₆H₄) exhibited the best inhibitory activity against most of the tested plant pathogenic fungi, demonstrating that this compound had certain broad-spectrum antifungal activity. In addition, a reasonable and effective 3D-QSAR model (r² = 0.994, q² = 0.529) was established using the comparative molecular field analysis (CoMFA) method to study the relationship between the structures of the target compounds and their antifungal activity against Physalospora piricola. Meanwhile, the results of electrostatic potential calculation of the compounds indicated that the electronic effect caused by different substituents on the benzene ring might be one of the factors affecting antifungal activity. In addition, frontier molecular orbital calculations implied that the anethole moiety and the thiazolinone-hydrazone-benzene structure in the target compounds might play an important role in antifungal activity. The potential binding mode between the target compound 5q (R = m-OH C₆H₄) and the homology-modeled succinic dehydrogenase was explored by molecular docking. Full article

Quantitative structure–activity/property relationship (QSAR/QSPR) is a well-established methodology widely used to model molecular properties based on structure and is applied in fields such as drug design and environmental protection. The knowledge and procedures developed and used in QSPR modelling will be applied to the validation of protein folding rate models. Understanding the protein folding process is considered one of the most important scientific topics, and identifying the fundamental factors responsible for protein folding has been the subject of intensive research over the past 30 years. Among the structural descriptors determining the protein folding rate, the length of the protein sequence, the content of regular secondary structures, and the average contact row distance between amino acids in the 3D structure are the most important. Comparative studies of different methods for predicting protein folding rates are occasionally published, and we conducted one such study. We found that the experimental data in literature databases and the data available online are inconsistent and scattered. This is partly due to differences in experimental data and protein sequence lengths, but more so due to the questionable quality of the models themselves. We observed very large deviations in the predictions of ln(k_f) by some of the analysed models implemented as web servers. The root mean square errors (RMSEs) of some of the analysed models in predicting ln(k_f) for a new external set of proteins are much larger than the RMSEs obtained for the same models on the training sets. External validation demonstrates that protein folding rate models available on web servers have accuracy for external protein sets comparable to that of a simple model based solely on the logarithm of protein chain length. This finding, which highlights the importance of external model validation as recommended by the OECD guidelines for QSAR validation, is fundamental and offers a new perspective for improving protein folding rate models by applying the knowledge and procedures used in the QSPR methodology. Full article

Monoenoic fatty acids (MUFAs), defined by the presence of a single carbon–carbon double bond within a long aliphatic chain, constitute a structurally diverse and ecologically significant class of lipids widely distributed in aquatic organisms. In marine and freshwater environments, MUFAs are fundamental components of membrane phospholipids and storage lipids, where mono-unsaturation modulates melting point, lipid packing, and bilayer dynamics, enabling homeoviscous adaptation to fluctuations in temperature, pressure, salinity, and oxygen availability. Positional and geometric isomerism (e.g., cis-Δ5, Δ7, Δ9, Δ11, Δ13, and trans forms) further enhances biochemical diversity, providing sensitive chemotaxonomic markers and indicators of trophic transfer across food webs. In addition to common straight-chain monoenes, rare methyl-branched, cyclopropane-containing, and acetylenic derivatives occur in specialized aquatic taxa, reflecting evolutionary adaptation and ecological niche differentiation. Computational QSAR analyses suggest that monoenoic fatty acids and their unusual analogues occupy bioactivity spaces associated with lipid metabolism regulation, vascular and inflammatory modulation, antimicrobial defense, and membrane stabilization. This review integrates structural chemistry, biosynthesis, ecological distribution, trophic dynamics, and predicted biological activity of monoenoic fatty acids in aquatic systems, highlighting their dual role as adaptive membrane constituents and as biologically active mediators linking molecular lipid architecture to hydrobiological function and environmental change. Full article

Background/Objectives: Feature selection approaches should satisfy all evaluation criteria required by state-of-the-art chemoinformatic models. Our aim is to develop a methodology that is robust, interpretable and computationally efficient. Methods: This study presents a robust methodology for developing highly interpretable and computationally efficient predictive models, with a specific application in the discovery of SARS-CoV-2 main protease inhibitors. We evaluated various descriptor selection procedures to identify a transparent and reproducible approach that provides actionable insights for data-driven decisions. The models were trained and tested using molecules from the CHEMBL database and further validated on an external set of compounds. Results: Our findings demonstrate that a recently proposed procedure, combining the FeatureWiz algorithm with stepwise feature selection, is the only approach that satisfies all evaluation criteria required by state-of-the-art chemoinformatic models. In particular, we found that models based on two-dimensional descriptors and Ordinary Least Squares regression achieved the best results. Conclusions: Our framework and the choices made offer significant advantages in a decision-making context due to their inherent interpretability and computational efficiency. Our derived models, benchmarked against those in the literature, serve as effective, transparent tools for the rapid and reliable prediction of biological activity, providing a validated framework for data-driven decisions in drug discovery and beyond. Full article

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R²_train = 0.9182, R²_test = 0.9306, and R²_global = 0.9208 with low prediction errors RMSE_train = 4.7888%, RMSE_test = 4.5485%, and RMSE_global = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R²_train/R²_test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Full article

Sustainable food production systems based on the use of biofertilizers and soil improvers are proposed to mitigate agricultural-related environmental impacts and address the climate crisis. In particular, plant growth-promoting microbes (PGPM) and biochar (Char) have been reported to improve plant growth, soil quality, and crop yield; however, their effects on food quality remain debated. In this study, untargeted metabolomics based on ultra-high performance liquid chromatography–ion mobility–high-resolution mass spectrometry (UHPLC-IMS-HRMS) and proton nuclear magnetic resonance spectroscopy (¹H-NMR) are proposed to achieve a comprehensive investigation of the effects of Char, PGPM and Char+PGPM on durum wheat. A total of 88 metabolites were annotated by UHPLC-IMS-HRMS, mainly belonging to carbohydrates, flavones, flavonoids, glycerophospholipids, and glycolipids, while 30 compounds were annotated by ¹H-NMR, mostly amino acids and short-chain carboxylic acids. The two datasets were merged with the gluten protein content dataset by using low- and mid-level data fusion approaches, obtaining models that exhibit excellent classification performance. Integrated analysis highlighted that the combined Char+PGPM treatment induced metabolic changes across multiple chemical classes, including enrichment of flavonoids and lipids, and downregulation of carbohydrate metabolites, suggesting a redistribution of carbon resources and modulation of secondary metabolism with potential implications on wheat grain quality. Full article

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure–activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm–multiple linear regression (GA–MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure–activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery. Full article

Endocrine-Disrupting Chemicals (EDCs) disrupt thyroid hormone (TH) synthesis, transport, metabolism, and action, thereby perturbing systemic endocrine homeostasis. Transthyretin (TTR) is a key TH transport protein that regulates circulating hormone distribution and tissue availability, particularly during critical developmental windows. Chemical interference with TTR-binding may alter TH bioavailability and represent a transport-mediated molecular initiating event within thyroid-axis perturbation. Despite widespread exposure, many thyroidal EDCs remain unidentified, and their health effects are difficult to assess due to multiple simultaneous exposures. To support endocrine hazard identification and chemical prioritization within risk assessment frameworks, we developed machine learning-based QSAR models during the Tox24 challenge, using a dataset of 1512 chemicals to predict TTR-binding affinity. Of these, 67% were used for training, 13% for testing, and 20% for validation. Molecular descriptors were selected by first removing highly correlated features and then ranking the remaining descriptors using mutual information regression. The leverage approach was applied to define the models’ applicability domain (AD). Five machine learning algorithms, including gradient boosting regressor (GBR), Random Forest, Lasso Regression, Support Vector Machine (SVM), and regularized SVM models, were developed. The GBR model demonstrated the best overall performance. This model achieved an R² of 0.89 on the training set, 0.58 on the test set, and 0.55 on the validation set. The molecular descriptor analysis highlights hydrophobicity, steric effects, branching, connectivity, and ionization/electronic effects as the mechanistic basis for TTR disruption and stabilization, providing structural insight into features associated with thyroid hormone displacement. The AD analysis indicated that 97.5% of the test set and 96.0% of the validation set fell within the reliable descriptor space. Importantly, these predictions extend beyond model benchmarking by informing weight-of-evidence evaluations of thyroid-axis perturbation and supporting the prioritization of chemicals for targeted testing within non-animal new approach methodologies. Overall, this work highlights the application of in silico approaches for screening EDCs, supporting the prioritization and identification of potentially harmful chemicals. Full article

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article

Fluopyram is a widely used nematicide with a growing number of varieties registered both domestically and overseas. However, its absorption, transportation, and metabolism behaviors in plants have not been fully elucidated, thus hindering comprehensive assessment of the risks associated with its use. This study investigated the plant uptake, distribution, and metabolic behavior of fluopyram through 168 h hydroponic experiments. Fluopyram was easily absorbed by the roots of the tested crops, and almost 90.5% and 70.9% of fluopyram was transformed in cucumber and tomato, respectively, leading to the tentative identification of 16 metabolites using Quadrupole Time-of-Flight mass spectrometry. The metabolic reactions involved were hydroxylation, hydroxylation–dechlorination, dehydrogenation, dechlorination, and glucuronidation conjugation. Most metabolites were detected in leaves, suggesting that they have considerable potential to accumulate in the upper parts, even the edible parts. Model prediction indicated that fluopyram and high-toxicity metabolites (M430A, M412C) pose significant risks to aquatic ecosystems across trophic levels, while M574A and M574B showed reduced toxicity due to glucuronidation conjugation. These findings deepen our understanding of the behavioral characteristics of fluopyram within plants, and serve as an important reference for comprehensively assessing its risks. Full article

Background/Objectives: The stimulator of interferon genes (STING) pathway plays a central role in innate immune signaling and represents an attractive therapeutic target for cancer immunotherapy. Amidobenzimidazole (ABZI) derivatives have emerged as promising non-nucleotide STING agonists with improved drug-like properties compared to cyclic dinucleotides. However, current ABZI compounds still exhibit limited oral bioavailability and cross-species potency discrepancies. In addition, potential systemic toxicity remains a concern, indicating the need for further structural optimization. Methods: In this study, a comprehensive computer-aided drug design strategy was employed to systematically investigate ABZI derivatives and identify novel STING agonists with enhanced activity and favorable pharmacokinetic profiles. A 3D quantitative structure–activity relationship (3D-QSAR) model was constructed using the Topomer CoMFA approach based on a dataset of 109 reported ABZI compounds. Guided by the contour map analysis, new chemical groups were introduced through a fragment growth method, generating a large virtual library. The library was subsequently filtered via molecular docking, molecular dynamics simulations, and MM-PBSA binding free energy calculations. Results: Among the newly designed ABZI compounds, five compounds displayed lower binding free energies than D59, with M13 and M44 showing reductions exceeding 6.7 kcal/mol. This work demonstrates the effectiveness of an integrated in silico design strategy for the discovery of novel STING agonists. Conclusions: The identified compounds represent promising candidates for subsequent experimental validation and may support the development of nitrogen heterocycle-based STING agonists for antitumor applications. Full article

Algae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges. Computational strategies, including virtual enzymatic hydrolysis, machine-learning prediction, QSAR modeling, molecular docking, molecular dynamics, and toxicity/allergenicity assessment, offer promising avenues for efficient peptide discovery, though their use in algae is still limited. Overall, this review underscores the potential of algae-derived proteins and peptides as multifunctional ingredients and emphasizes the need to integrate in silico pipelines with improved processing and delivery systems to accelerate future translational applications. Full article

Traditional drug design methods based on trial and error are costly and inefficient. The computational approach ToSS-MoDE (Topological Substructural Molecular Design) offers an alternative by linking molecular descriptors to biological activity. To develop a QSAR model to predict the anti-inflammatory activity of synthetic and natural compounds using weighted spectral moments. Spectral moments (µ_k) were calculated from the adjacency matrix between bonds for 410 compounds (180 active and 230 inactive). MODESLAB software (MICROSOFT OFFICE 365) was used to generate descriptors, and Linear Discriminant Analysis (LDA) was applied to classify activity. The model was validated with an external series of 62 compounds. Results. The model showed an overall classification of 91.59% in the training series and 90.2% in validation. The spectral moments µ₀, µ₃, µ₄, and µ₅ were the most significant. Diosgenin, a natural metabolite, showed potential anti-inflammatory activity (classification probability: 81%). The model showed strong training performance (91.7% accuracy) and promising external performance for confidently classified compounds. All datasets, descriptor-generation settings, coefficients, and posterior probabilities are fully described in the main text to ensure full reproducibility. Full article

Numerous scientific studies highlight the crucial role of common genetic and epigenetic factors in the development and progression of cancer. To deepen our understanding of how different VEGFR and epigenetic pathways interact in carcinogenesis, the current review examines novel therapeutic agents that target various molecular mechanisms involved in this complex disease. Growing evidence from scientific studies suggests that VEGFR and epigenetic signaling pathways contribute to complex pathophysiological changes in cancer. Therefore, simultaneously targeting VEGFR and epigenetic factors, such as sirtuins, by developing dual inhibitors could provide more individualized therapeutic approaches with safer and more effective outcomes. In this context, Computer-Aided Drug Design (CADD) offers a comprehensive suite of bioinformatic, chemoinformatic, and chemometric approaches to design novel chemotypes of epigenetic dual-target inhibitors. This facilitates the efficient discovery of new drug candidates, enabling innovative treatments for these multifactorial diseases. The review also explores the detailed anticancer mechanisms by which VEGFR, SIRT, and dual-target inhibitors modify metastatic and tumorigenic properties, affect the tumor microenvironment, and regulate the immune response. Full article