Search Results (154)

This study evaluates the chemical profile and antifungal efficacy of essential oils from Piper glabratum, Piper friedrichsthalii, and Piper cumanense against the cocoa pathogens Moniliophthora roreri and Phytophthora palmivora. Microwave-assisted hydrodistillation followed by GC-MS analysis identified 80 constituents, predominantly monoterpenes and sesquiterpenes, which exhibited significant mycelial inhibition comparable to commercial fungicides. Beyond basic characterization, a comprehensive chemoinformatic analysis was conducted to elucidate the molecular mechanisms driving this bioactivity. The computed physicochemical landscape reveals a dominant lipophilic profile (average LogP 3.4) and low polarity (TPSA 11.5 Ų), characteristics essential for effective fungal membrane penetration. Structural mining identified conserved benzene and cyclohexene scaffolds alongside specific 1,3-benzodioxole moieties, while Maximum Common Substructure (MCS) analysis uncovered high similarity clusters among phenylpropanoids and sesquiterpenes. These findings suggest a synergistic mode of action where conserved structural backbones and interchangeable diastereomers facilitate membrane destabilization and ion leakage. Consequently, the integrative chemoinformatic profiling elucidates the molecular basis of this efficacy, positioning these Piper essential oils not merely as empirical alternatives, but as sources of rationally defined synergistic scaffolds for next-generation sustainable fungicides. Full article

Breast cancer (BC) is a malignant tumor that develops in the mammary gland due to uncontrolled cell proliferation. Estrogen receptor (ER) signaling, mediated by 17β-estradiol (E2), plays a crucial role in regulating cell proliferation, differentiation, and survival. Specifically, the binding of E2 to the estrogen receptor alpha (ERα) increases cell proliferation. Conversely, selective estrogen receptor beta (ERβ) agonists inhibit cancer cell proliferation by suppressing the expression of oncogenes, making ERβ an important therapeutic target. Given the urgent need for targeted and effective therapies for BC, we implemented a strategy based on multicomplex pharmacophores modeling of ERβ (MPMERβ) and ERα (MPMERα), performing a virtual cross-screening of databases of clinically approved and experimental drugs to identify those with high affinity and stereoelectronic complementarity with the ERβ agonist pharmacophore hypothesis. The implementation of a chemoinformatic strategy enabled the identification of Sobetirome, Labetalol, and Procaterol as molecular hits on the ERβ pharmacophore map. Procaterol showed the most significant antiproliferative activity in vitro assays, with IC₅₀ values of 21.26 and 36.10 µM in MCF-7 and MDA-MB-231, respectively. It is imperative to note that these findings require experimental validation of the ERβ activation pathways to strengthen the possible therapeutic repurposing of the drugs selected through our in silico approach. Finally, this strategy not only facilitates drug repurposing under in silico simulation but also provides valuable information for the rational design of new drugs against BC. Full article

Developing predictive models for drug efficacy is challenged by the complexity and heterogeneity of bioassay data. Here, we present LIFE.PTML, which is a methodology integrating drug Lifecycle (L), Information Fusion (IF), Encoding (E), Perturbation Theory (PT), and Machine Learning (ML) to predict compound activity across diverse experimental conditions. Using a dataset of 3748 molecule–assay combinations targeting calmodulin (CaM) and related proteins, LIFE.PTML combines chemical and protein descriptors, quantifies experimental variability via perturbation operators, and trains non-linear classifiers, including XGBoost and Gradient Boosting. XGBoost achieved the best performance, with 88.9% test accuracy and an ROC AUC of 0.959, while feature importance analysis highlighted contributions from both drug- and protein-level descriptors. The results demonstrate that LIFE.PTML provides a robust, flexible, and interpretable framework for predictive chemoinformatics, facilitating the integration of multi-source data for drug discovery applications. Full article

Perezone, a sesquiterpene quinone, was the first natural product isolated in crystalline form on the American continent in 1852. It is commonly found in the roots of herbs from the Acourtia species (formerly Perezia). This molecule, along with its synthetic isomer isoperezone, exhibits antineoplastic effects, among others. In this study, an enzymatic reaction (green chemistry) was employed to oxidize the C12−C13 double bond of perezone and isoperezone. This method proved to be more effective than traditional toxic chemical oxidants. As result, epoxides were obtained, followed by acetonides, diols, and esters. All compounds were successfully synthesized and characterized using standard spectroscopic techniques. In breast cancer cell tests, the isoperezone acetonide showed the highest cytotoxicity, with an IC₅₀ of 8.44 µM. Additionally, a computational study was performed at the DFT (B3LYP) level of theory, indicating that the geometrical and energy differences between 6-R and 6-S stereoisomers are 0.5 kcal/mol, and the spectroscopic and electronic properties aligned with the experimental data. Finally, molecular docking revealed binding energies of −8.14 kcal/mol for 6-R and −8.04 kcal/mol for 6-S, with a hydrogen bond of 2.9 Å involving the His121 residue. A chemoinformatic prediction was also conducted to compare cytotoxicity results. Full article

Visceral leishmaniasis caused by Leishmania donovani is one of the major neglected tropical diseases attributable to parasitic protozoa. In the absence of an effective vaccine, chemotherapy remains the only available therapeutic option. However, current treatments rely on a limited number of drugs that are largely obsolete, highly toxic or require intravenous administration, and their extensive use has led to the emergence of drug resistance. Consequently, the discovery of new antileishmanial agents is an urgent priority. In this study, a commercial library of 449 alkaloids in a high-throughput screening format was evaluated against both axenic bone marrow-derived amastigotes and intramacrophagic amastigotes from mice infected with L. donovani IRFP, a strain engineered to emit infrared fluorescence in its viable form. Six isoquinoline-type alkaloids showed the best antileishmanial efficacy against intramacrophagic amastigotes while exhibiting minimal cytotoxicity toward RAW 264.7 and HepG2 cell lines, with a promising selective index higher than four, and good mouse intestinal tolerance in mouse organoids. Among these compounds, the protoberberine scaffold emerged as the most promising candidate for further drug development. Full article

The invasive plant Heracleum sosnowskyi Manden. is a valuable source of a number of bioactive metabolites that can be used in the pharmaceutical industry and medicine and may have some other applications as well. Today, there is a need to summarize data on these substances as well as analyze the toxicological profile of the metabolites of H. sosnowskyi. In this study, we collected a dataset of 225 metabolites of H. sosnowskyi from different literature sources and performed cluster analysis of their chemical structures; we revealed five main clusters of compounds: terpenoids, aromatic compounds, polyaromatic compounds, fatty acids, and furanocoumarins. In order to fill the gaps in the experimental data on the toxicity of the studied substances, we used machine learning (ML) algorithms previously designed for high-accuracy prediction of toxicity end-points. The ML-based approach allowed us to fill in up to 90% of the missing median lethal dose $L{D}_{50}$ (mouse) data for the studied molecules. The validity of each predicted value was confirmed by analyzing the applicability domain of the used ML models. For the calculations and ML modeling, we used the Syntelly chemoinformatics platform. For the most toxic compounds—hydroxycoumarins and furanocoumarins of H. sosnowskyi—the values for hepatotoxicity, drug-induced liver injury (DILI), cardiotoxicity, and carcinogenicity were predicted. Based on the analysis of $L{D}_{50}$ values for the mouse animal model, the greatest toxicity for furanocoumarins is expected with the intravenous route of administration (62–450 mg/kg), which can cause drug-induced liver injury. At the same time, the data do not show high cardiotoxicity risks for the studied furanocoumarins. Based on the presented results, we discuss prospects of using some of the compounds as pharmaceutical agents. Full article

The urgent need to reduce the cost of new drug discovery has led us to create a new, more selective screening method using free chemoinformatics tools to restrict the high failure rates of lead compounds (>90%) during the development process because of the lack of clinical efficacy (40–50%), unmanageable toxicity (30%), and poor drug-like properties (10–15%). Our efforts focused on new molecular entities (NMEs) with reported activity as tyrosine kinase inhibitors (small molecules) as a class of great potential. The criteria for the new method are acceptable Druglikeness, desirable ADME (absorption, distribution, metabolism, and excretion), and low toxicity. After a bibliographic review, we first selected the 29 most promising compounds, always according to the literature, then collected the in silico calculated data from different platforms, and finally processed them together to conclude at 14 compounds meeting the aforementioned criteria. The novelty of the present screening method is that for the evaluation of the compounds for Druglikeness, and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), the data of the different platforms were used as a whole, rather than the results of each platform individually. Additionally, we validated our new consensus-based method by comparing the final in silico results with the experimental values of FDA (Food and Drug Administration)-approved tyrosine kinase drugs. Using inferential statistics of 39 FDA-approved tyrosine kinase drugs obtained after applying our method, we delineated the intervals of the desired values of the physicochemical properties of future active compounds. Finally, molecular docking studies enhance the credibility of the applied method as an identification tool of Druglikeness. Full article

Nineteen previously and newly synthesized amphoteric 8-[(dialkylamino)methyl]-7-hydroxy-4-(2-oxo-2H-chromen-3-yl)-2H-chromen-2-ones were assayed as inhibitors of monoamine oxidases (MAO-A and B) and cholinesterases (AChE and BChE). Five of the tested compounds (2b, 2c, 3c, 5b, and 5c), namely those bearing the less bulky alkyls in the Mannich base 8-CH₂NR₂ (R = Me, Et) and the halogens (Cl, Br) at C6 of the 4-coumarin-3-yl moiety, showed moderate inhibitory potencies toward human MAO-A in the single-digit micromolar range (IC₅₀s from 1.49 to 3.04 µM). In particular, the 6′-Cl derivatives 2b and 5b proved to be reversible competitive inhibitors of human MAO-A with K_i values of 0.272 and 0.326 µM. Among the tested compounds, 3c proved to also be a moderate inhibitor of human AChE (IC₅₀ 4.27 µM). Molecular docking calculations suggested binding modes of the most active compounds to MAO-A and AChE binding sites consistent enough with the experimental data. Chemoinformatic tools suggest for the most active compounds, including the dual MAO-A/AChE inhibitor 3c, full compliance with Lipinski’s rule of five, high probability of gastrointestinal absorption, but low blood–brain barrier (BBB) permeability. While further efforts are required to improve their CNS distribution, herein new phenolic Mannich bases have been identified that may have potential for treating neurodegenerative syndromes. Full article

(1) Background: Medicinal plants are widely used in folk medicine. Hypericum perforatum L. (St. John’s wort) is a medicinal plant that is used domestically and exported to other countries. This study addresses the need to develop methods for determining the composition and content of St. John’s wort to determine its biological activity. (2) Methods: High-performance liquid chromatography (HPLC) equipped with an Electrochemical Detector (ECD) and a Photodiode Array Detector (PDA) was employed to identify and quantify major phenolic compounds—gallic acid, catechin, epicatechin, hyperoside, quercetin, and hyperforin—in extracted and lyophilized St. John’s wort flower; stem; and leaf samples. Key analytes exhibited linear responses across both detection systems, within a quantification range of 0.5–10 µg/mL. (3) Results: The PDA method, validated according to ICH Q2(R1) guidelines, demonstrated specificity, linearity, precision, and accuracy, with limits of detection (LOD) ranging from 0.24 to 0.61 µg/mL and limits of quantification (LOQ) between 0.26 and 0.62 µg/mL. PDA effectively identified gallic acid, epicatechin, hyperoside, quercetin, and hyperforin, although catechin was not detected. ECD yielded comparable compound levels across the samples. (4) Conclusions: The novelty of this study lies in identifying the influence of climatic factors associated with the altitude at which St. John’s wort is grown on the content and ratio of biologically active components. Overall, the chemometric approach demonstrates the utility of raw chromatographic data in distinguishing samples by plant part and geographic origin; even when traditional compound-based comparisons may be limited. Full article

The pressing global challenges of parasitic diseases, particularly prevalent in tropical and subtropical regions, underscore the critical urgent need for innovative therapeutic strategies in identifying and developing new treatments. The immense chemical diversity inherent in nature has rendered natural product (NP) chemistry a promising avenue for the discovery of novel antiparasitic chemotypes. Despite challenges such as sourcing, synthetic complexity, and drug resistance, NPs continue to offer invaluable contributions to antiparasitic therapy. This review focuses on recent advancements in NP chemistry and their application in the development of antiparasitic therapeutics. Key highlights include the identification of new molecular targets such as enzymes, membrane proteins, and metabolic pathways in parasites, as well as the role of metabolomics, genomics, and high-throughput screening in accelerating drug development. Additionally, the exploration of microorganisms (including soil bacteria and fungi) and marine organisms as a latent reserve of bioactive compounds with potent antiparasitic activity is discussed. The review further examines emerging strategies such as chemoinformatics and combination and polypharmacology therapies, aimed at addressing the challenges of antiparasitic chemotherapeutic treatment and advancing the development of new and effective treatments. Ultimately, NP chemistry represents a frontier for the design of novel antiparasitic drugs, offering the potential for more effective and sustainable therapies for combating parasitic diseases. Full article

Background: The sustainable use of agro-industrial by-products is essential to reduce environmental impact and enhance resource efficiency. In this study, white grape skins (WGSs), a distillation by-product of grappa production, are valorized through the development of an eco-friendly extraction process. Methods: At the laboratory scale, water-based and hydroalcoholic extractions are evaluated, prioritizing the water-based method due to its better scalability and eco-sustainability. Furthermore, this green extraction method enables industrial scale-up by Distillerie Bonollo Umberto S.p.A. (Mestrino, Italy), resulting in Vituva^®, an industrial extract with a composition comparable to its water-based laboratory counterpart. LC-HRMS-based targeted metabolomics identified 50 metabolites in the hydroalcoholic extract, 36 in the water-based extract, and 37 in the industrial extract, which included mainly polyphenols such as flavonoids and phenolic acids. Results: In vitro assays show that the water-based and industrial extracts exhibit significant anti-inflammatory activity, especially in gastric epithelial cells, while the hydroalcoholic extract displays stronger antioxidant activity via Nrf2 pathway activation but was more cytotoxic, possibly due to polyphenol-induced hormesis. Notably, the industrial extract also activates Nrf2 to a lesser extent, supporting its dual bioactivity profile. Chemoinformatic and statistical analyses support the identification of the likely mechanisms of action. Conclusions: Overall, this work demonstrates how green chemistry and circular economy principles transform a waste product into a high-value bioactive ingredient. Full article

Modern dentistry depends on polymer composite materials for a wide range of applications. These materials, mainly composed of polymer resins and reinforced with inorganic fillers, offer mechanical strength, wear resistance, and durability for restorations and prosthetics. This study concentrated on the density and surface tension of monomers often used in dental resins and employed Quantitative Structure–Property Relationship (QSPR) modeling to investigate the influence of monomers’ structural features on these properties. Two main and two auxiliary models to predict both density and surface tension were built and validated. Additionally, two models based on CircuS descriptors were built and analyzed. Molecular descriptors from the models were interpreted and structural characteristics of dental monomers influencing their physicochemical properties were identified. It was found that the presence of heteroatoms increases both of the analyzed properties, while all of the other identified structural features exert an opposite influence on density and surface tension. Furthermore, the study showed that the density of dental monomers can be reliably predicted using the database containing regular organic compounds, but the surface tension requires the database containing specific monomers in order to perform satisfactorily. Full article

Monoterpenoids are a structurally diverse class of natural products with a long-standing history of therapeutic use. Despite their promising bioactivities, their clinical development has been limited by dose-dependent toxicities, poor pharmacokinetics, and suboptimal drug-like properties. In this work, a comprehensive in silico pipeline was employed to evaluate 1175 monoterpenoid compounds retrieved from ChEBI, aiming to identify structurally diverse candidates that possess favorable drug-like characteristics. A total of 54 molecular parameters were calculated using thirteen computational tools, covering physicochemical parameters, ADMET profiles, and toxicological risk assessments. Stepwise filtering was employed to retain only compounds meeting stringent thresholds across multiple domains, followed by chemoinformatic analysis. Structure–activity relationship mapping and target prediction were subsequently conducted to explore mechanistic plausibility. This workflow led to the identification of seven top-performing monoterpenoids that exhibited ideal physicochemical profiles, high gastrointestinal absorption, low predicted toxicity, and full compliance with medicinal chemistry rules. Notably, target prediction revealed a convergence on GPCRs, enzymatic and nuclear receptors, highlighting potential anti-inflammatory and neuromodulatory effects. The identification of conserved pharmacophores across selected scaffolds further reinforces their translational potential. Our results highlight the value of multi-parameter computational triage in natural product drug discovery and reveal a subset of overlooked monoterpenoids with promising preclinical applications. Full article

Background: Prostate cancer is the most common cancer in men, especially after the age of 50. It is a malignant disease that is increasing due to the increased life expectancy of the world population. Its development and progression are dependent on androgenic stimulation. Objectives: This study aimed to identify potential inhibitors with anti-prostate cancer activity through the application of chemoinformatics tools, exploring the Princeton (~1.2 million compounds) and Zinc Drug (~175 million compounds) databases. Methods: The methodology used several computational techniques, such as ROCS (Rapid Chemical Structure Superposition) and EON (Electrostatic Potential Screening), predictions of pharmacokinetic and toxicological properties, molecular docking, synthetic accessibility, biological activity, and molecular dynamics. Results: At the end of all these virtual screening steps, the study resulted in four promising potential candidates for the treatment of prostate cancer: the molecules ZINC34176694, ZINC03876158, ZINC04097308, and ZINC03977981, which exhibited all the desirable pharmacokinetic parameters (ADME/Tox) for a potential drug. Conclusions: Docking and molecular dynamics studies demonstrate stability and interaction with the androgen receptor of the selected compounds, showing them to be promising candidates for the development of new drugs. Full article

(1) Background: Although the field of natural product (NP) drug discovery has been extensively developed, there are still several bottlenecks hindering the development of drugs from NPs. The PD-1/PD-L1 immune checkpoint axis plays a crucial role in immune response regulation. Therefore, drugs targeting this axis can disrupt the interaction and enable immune cells to continue setting up a response against the cancer cells. (2) Methods: We have explored the immuno-oncological activity of NPs targeting the PD-1/PD-L1 immune checkpoint by estimating the half maximal inhibitory concentration (IC₅₀) through molecular docking scores and predicting it using machine learning (ML) models. The LightGBM (Light Gradient-Boosted Machine), a tree-based ML technique, emerged as the most effective approach and was used for building the quantitative structure–activity relationship (QSAR) classification model. (3) Conclusions: The model incorporating 570 spectral descriptors from NMR SPINUS was selected for the optimization process, and this approach yielded results for the external test set with a sensitivity of 0.74, specificity of 0.81, overall predictive accuracy of 0.78, and Matthews correlation coefficient (MCC) of 0.55. The strategy used here for estimating the IC₅₀ from docking scores and predicting it through ML models appears to be a promising approach for pure compounds. Nevertheless, further optimization is indicated, particularly through the simulation of the spectra of mixtures by combining the spectra of individual compounds. Full article