Search Results (1,148)

Background/Objectives: KRAS G12C mutations define a clinically actionable subset of solid tumors, particularly non–small cell lung cancer. Clinical responses to approved covalent inhibitors remain limited by intrinsic and acquired resistance, highlighting the need for structurally distinct inhibitor scaffolds to expand therapeutic options. The objective of this study was to identify novel covalent binders targeting the KRAS G12C switch-II pocket through large-scale in silico screening and experimental validation. Methods: More than 1.9 million small molecules from diverse commercial libraries were screened using covalent docking, followed by multi-stage refinement incorporating molecular dynamics simulations, MM/GBSA free-energy estimation, and cancer-focused QSAR modeling. Results: This integrated workflow yielded 50 prioritized compounds spanning several chemically distinct scaffold classes. These candidates displayed favorable predicted binding energetics, stable ligand-protein interactions over extended simulation timescales, and low structural similarity to clinically approved KRAS G12C inhibitors sotorasib and adagrasib. Benchmarking against these clinical agents, using identical computational parameters, yielded comparable predicted binding energies for several candidate molecules. In cellular NanoBRET target-engagement assays, selected scaffolds, including K788-7251 and AN-989/14669131, exhibited sub-micromolar engagement of KRAS G12C with minimal endothelial cytotoxicity. Conclusions: Collectively, these findings identify structurally distinct, KRAS G12C inhibitor chemotypes and provide tractable starting points for the development of next-generation targeted therapies. Full article

Assessing genotoxicity, specifically gene mutations and chromosomal aberrations, is fundamental to chemical risk assessment. Notably, the early identification of an aneugenic mechanism is of crucial importance, allowing, in principle, for a threshold-based risk assessment approach. To investigate this issue while pushing towards innovation in risk assessment by leveraging New Approach Methodologies, in silico approaches stand out as a particularly promising avenue. Building on these premises and given the lack of QSAR models for aneuploidy in the public domain, the present study exploited the genotoxicity-relevant alert lists implemented in the OECD QSAR Toolbox to base the investigation of structure-activity relationships for aneuploidy. To address the lack of relevant structured data resources, a dataset of 65 confirmed aneugenic substances was specifically curated and designed for the study. The results highlighted widely differing performances among the various profilers, confirming a general limited discriminatory power for aneuploidy. On the other hand, a granular analysis of the results from individual structural alerts enabled the successful isolation of some features associated with the aneugenic mode of action. Moreover, a subset of tubulin-binding chemicals was investigated to determine whether targeting a specific protein improves the characterization of toxicological alerts. The findings provide a refined definition of specific toxicity determinants for tubulin binders and serve as a promising tool for early hazard assessment, potentially informing relevant AOPs. While the computational approach appears promising, the overarching challenge that emerges is the limited availability of well-curated experimental data. In fact, reliable data on aneuploidy are scarce and fragmented across the literature. Furthermore, existing compilations of micronucleus study results are often complicated by conflicting interpretations. Full article

Food safety, nutrition, and public health are actual economic and medical problems. Sweetness is an important feature of food technology. Models for the sweetness of special organic compounds used in the food industry are suggested. The models are built using the CORAL software. New statistical coefficients of predictive potential are studied. These are the index of ideality of correlation (IIC) and correlation intensity index (CII). The effectiveness of using the IIC and CII has been tested in simulated sweetness via Monte Carlo optimization of correlation weights for molecular features extracted from Simplified Molecular Input Line Entry System (SMILES) strings. Both factors have been shown to improve the model’s statistical quality on the calibration and validation sets. However, this is accompanied by a decrease in the statistical quality of the training sets. Full article

Imidazolinone herbicides such as imazethapyr (IMT) pose potential ecological risks due to their high mobility and ecotoxicity. This study synthesized the bismuth-based photocatalyst BiOIO₃ via a facile hydrothermal method and systematically characterized its physicochemical properties. BiOIO₃ features a 2D lamellar structure, pure phase composition, and a built-in internal polarization electric field that efficiently separates photogenerated electron–hole pairs. Photocatalytic experiments exhibited that BiOIO₃ achieved 84.5% elimination of IMT, with a rate constant 66 times higher than that of TiO₂ (Rutile). Mechanistic studies revealed that photogenerated electrons (e⁻), holes (h⁺), and superoxide radicals (·O₂⁻) are the primary reactive species. HPLC-MS/MS identified key intermediates, and QSAR-based toxicity prediction showed reduced mutagenicity for most intermediates. Importantly, BiOIO₃ effectively eliminated five imidazolinone herbicides simultaneously. This work highlights BiOIO₃ as a promising photocatalyst for efficient and practical remediation of imidazolinone herbicide-contaminated water. Full article

Plants of the genus Salacia (Celastraceae) have long been used in traditional medical systems of South and Southeast Asia for the management of diabetes and related metabolic disorders. Modern phytochemical and pharmacological studies have confirmed the antidiabetic potential of several Salacia species, leading to the identification of a distinctive group of sulfur-containing sugars as their principal bioactive constituents. Salacinol, neosalacinol, kotalanol, neokotalanol, and related analogues represent a novel class of thiosugar sulfonium compounds that act as potent and selective α-glucosidase inhibitors, providing a clear mechanistic basis for their glucose-lowering effects. Simpler thiosugars, such as 5-thiomannose, further contribute to the overall metabolic activity of Salacia extracts and may serve as biosynthetic or functional precursors. Beyond Salacia, sulfur-containing natural products are widespread in nature and perform diverse biological roles. In particular, the genus Allium is well known for producing organosulfur compounds, including thioethers and polysulfides, which exhibit antidiabetic, hypolipidemic, antioxidant, and cardioprotective activities. In a different context, sulfur-containing hopanes have been identified in sediments and petroleum as products of early diagenetic sulfurization of bacterial hopanoids. Although these compounds have been studied primarily as geochemical biomarkers, recent QSAR/PASS analyses suggest that sulfur hopanes may also possess biologically relevant activities, particularly related to metabolic and cardiovascular regulation. Recent PASS-based QSAR evaluations of Salacia-derived thiosugars and sulfur hopanes predict significant antidiabetic activity, including potential type 2 diabetes-related pharmacological effects, supported by predicted α-glucosidase inhibitory, hypoglycemic, hepatic, and gastrointestinal activities. Collectively, these findings highlight sulfur-containing natural products from both plant and sedimentary sources as chemically diverse yet functionally convergent scaffolds with promising potential for the development of functional foods and therapeutic agents targeting metabolic disorders. Full article

Ten substituted quinobenzothiazinium salts were tested for their ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). All the compounds inhibited AChE in the IC₅₀ range of 0.03–0.658 µM, with 5,8,10-trimethyl-12H-quinolino[3,4-b][1,4]benzothiazin-5-ium chloride (3d) being the most potent inhibitor, with an IC₅₀ value significantly better than that of the clinically used rivastigmine and galantamine and comparable to that of tacrine and donepezil. The IC₅₀ values for BChE inhibition ranged from 0.34 to 4.25 µM; 5,9-dimethyl-12H-quinolino[3,4-b][1,4]benzothiazin-5-ium chloride (3b) exhibited the strongest BChE inhibitory activity and in general, all the investigated compounds were more potent inhibitors than rivastigmine and galantamine. Based on the calculated selectivity index values, they are rather preferential inhibitors of AChE. Cytotoxicity tests performed on normal human dermal fibroblasts (HFF-1) did not demonstrate any significant cytotoxicity under the tested conditions. The distance-oriented structure distribution for the studied molecules was related with the activity data using principal component analysis and hierarchical clustering analysis. (SAR)-based evaluation is reported to predict activity cliffs using a similarity–activity landscape index for the AChE inhibitory response values. Moreover, direct protein-mediated in silico methods were utilized to identify factors that may be relevant for quantitative (Q)SAR modeling. In practice, target-oriented molecular docking was used to organize the spatial distribution of the ligand property space for the anti-AChE system. In general, this series of alkylated quinobenzothiazinium salts with potent inhibitory activity against cholinesterases fulfills Lipinski’s rule of five based on in silico predictions and is also expected to have high absorption in the human gastrointestinal tract. All active derivatives are also expected to penetrate the blood–brain barrier, making them promising compounds for further research and possible use in Alzheimer’s disease therapy. Full article

The evaluation of the safety of chemical substances requires the identification of a safe dose, which has no adverse effects on humans. This is obtained through animal studies, with exposure prolonged for months. Repeated-dose toxicity is a term in toxicology and pharmacology referring to the highest tested dose of a substance, so-called No Observed Adverse Effect Level (NOAEL). Experimental data on NOAEL taken from the literature and the OpenFoodTox database (total n = 848). To speed up the processing of the enormous number of substances we are exposed to, in silico models are an attractive solution. Monte Carlo technique, incorporating the Las Vegas algorithm, was applied to develop models for repeated-dose toxicity in rats. Optimal descriptors were calculated using correlation weights for attributes of the Simplified Molecular Input Line Entry System (SMILES). Computational experiments were conducted 5 times, with splits obtained using the Las Vegas algorithm. Good predictive potential was observed for these models, with an average determination coefficient on the validation set of 0.77 ± 0.04. Full article

As the key enzyme catalyzing the final step in heme and chlorophyll biosynthesis, protoporphyrinogen oxidase (PPO) is a crucial target for herbicide development. To date, over 40 PPO inhibitors have been commercialized. They offer high efficacy, environmental safety, low application rates, and broad-spectrum weed control. Recently, significant progress has been made in PPO structural biology, with several crystal structures resolved. Despite decades of use, the emergence of resistant weeds necessitates the continuous innovation of novel PPO inhibitors. This review systematically summarizes PPO three-dimensional structures, enzyme-inhibitor interaction mechanisms, and quantitative structure–activity relationships (QSARs). Finally, we outline rational molecular design strategies for the next generation of PPO inhibitors. Full article

Background: Cysteinyl leukotrienes are components of slow-reacting substances of anaphylactic shock (SRS-A) and play a key role in asthma and inflammatory responses. Although chromone-2-carboxylic acids and substituted (aryloxy)alkanoic acids have the potential to be SRS-A antagonists, their comprehensive structure–activity relationships and pharmacokinetic characteristics remain understudied. Objective: This study integrated computational and experimental approaches, including QSAR modeling, molecular docking, ADMET analysis, molecular dynamics (MD) simulations, and antioxidant evaluation to identify and prioritize bifunctional compounds with anti-inflammatory and free radical-scavenging properties. Methods: A set of 68 compounds was analyzed using 2D and 3D quantitative structure–activity relationships (QSAR) (MLR, MNLR, SVR, ANN, and atom-based partial least squares). Molecular docking and 100 ns MD simulations were performed against the CysLT₁ receptor (PDB ID: 6RZ5). ADMET and drug-like properties of the compounds were predicted using ADMETlab 2.0 and SwissADME, and the in vitro antioxidant activity of the top-ranked compounds was evaluated using the DPPH method. Results: The atom-based 3D-QSAR model showed strong predictive power (R² = 0.9524, Q² = 0.5382). Compounds 25, 41, and 47 stood out with the most significant binding energies: −9.5 kcal/mol for 25, −10.0 kcal/mol for 41, and −9.4 kcal/mol for 47. MD simulations confirmed the structural stability and consistent interactions of the protein-compound 47 complex. ADMET analysis showed that compounds 25 and 41 had good pharmacokinetic properties, and in vitro antioxidant assays verified their free radical-scavenging efficacy. Conclusion: Our results highlight the utility of an integrated computational–experimental strategy for the discovery of dual-acting SRS-A antagonists. Compound 25 is highlighted as a promising lead compound for further preclinical development, which effectively combines leukotriene receptor antagonism and antioxidant activity. This framework provides an effective strategy for prioritizing lead compounds in anti-inflammatory drug development. Full article

Polyhydroxyalkanoates (PHAs) emerge as promising biodegradable polymers for sustainable applications, yet predicting their biodegradation behavior under different environmental conditions remains challenging. In this study, we propose a novel data-driven computational framework for predicting biodegradation-induced weight/mass loss in PHA-based materials. A comprehensive database of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)-based formulations was manually curated by systematically collecting and harmonizing material descriptors, environmental parameters, and experimental biodegradation outcomes from laboratory- and large-scale studies conducted in soil, marine, freshwater, and compost environments. Multiple regression-based quantitative structure–activity relationship (QSAR) models were developed and rigorously validated, demonstrating high predictive performance and strong correlations between polymer structure, environmental conditions and degradation behavior. “Exposure time”, “degradation environment” and “hydroxybutyrate (HB) ratio” were identified as the most important features for weight loss. Finally, the predictive model was integrated into the Jaqpot computational platform, enabling open access and facilitating data-driven assessment and design of biodegradable polymer systems. Full article

Background/Objectives: Leishmaniasis is a group of diseases caused by obligate intracellular parasites of the Leishmania genus and is classified by the World Health Organization as a category I neglected tropical disease. Leishmania infantum predominantly affects children under five years of age and shows an increasing incidence of cutaneous and visceral forms. The development of new therapeutic alternatives remains challenging, making in silico approaches valuable for accelerating antileishmanial drug discovery. This study aimed to identify new compounds with potential activity against Leishmania infantum amastigotes using artificial intelligence-based classification models. Methods: A curated database of compounds with reported biological activity was constructed. Molecular representation employed zero- to two-dimensional descriptors calculated with Dragon software (v 7.0.10). Unsupervised k-means cluster analysis was applied to define training and external prediction sets. Supervised models were developed on the WEKA platform using IBk, J48, multilayer perceptron, and sequential minimal optimization algorithms. Model performance was assessed through internal cross-validation and external validation procedures. Results: All models achieved classification accuracies above eighty percent for both training and prediction sets, indicating consistent predictive performance and good generalization ability. The validated models were applied to virtual screening of the DrugBank database and a collection of synthetic compounds. This screening campaign enabled the identification of one hundred twenty compounds with potential activity against the amastigote form of Leishmania infantum. Conclusions: Artificial intelligence-based QSAR models proved to be useful tools for prioritizing antileishmanial candidates. The integration of molecular descriptors, machine learning, and virtual screening offers an efficient strategy for drug discovery. Full article

Background/Objectives: Poly(ADP-ribose) polymerase 1 (PARP1) is an important therapeutic target in DNA repair-deficient cancers, but discovery of new inhibitors remains constrained by scaffold convergence, tolerability limits, and acquired resistance. This study aimed to develop an interpretable, reliability-stratified cheminformatics workflow for PARP1 potency prioritization and structure-based follow-up. Methods: A curated ChEMBL dataset of 3339 PARP1 inhibitors was encoded using RDKit 2D descriptors and Avalon fingerprints (1143 initial features), then reduced to 132 informative variables by Random Forest-based feature selection. Five regression models were optimized, including a stacked ensemble. Model interpretation was performed using permutation feature importance and SHAP. External near-domain corroboration was assessed using a stringent PubChem similarity expansion (Tanimoto > 0.90) around sub-10 nM seed compounds, followed by comparison with retrievable experimental PARP1 activity values. Top scaffold-diverse candidates were further evaluated by complementary docking against PARP1 (PDB: 4R6E) using AutoDock Vina and cavity-guided docking through the SwissDock platform. Results: The stacked ensemble achieved the best held-out performance (test R² = 0.723; RMSE = 0.610 pIC₅₀ units), with 83.7% of test predictions within ≤0.75 pIC₅₀ units and only 2.7% exceeding 1.5 pIC₅₀ units. PubChem similarity expansion retrieved approximately 32,450 analogs, of which 3349 were predicted to have IC₅₀ ≤ 10 nM. Among 366 compounds with retrievable experimental PARP1 activity values, predicted versus experimental pIC₅₀ showed a positive association (R² = 0.124; Pearson r = 0.479), with RMSE = 0.491 and MAE = 0.330 pIC₅₀ units. Three ligands—CID 168873053, CID 175154210, and CID 172894737—showed the strongest complementary docking support and pocket-consistent poses relative to niraparib. Conclusions: This workflow provides a transparent and practically useful framework for near-domain PARP1 inhibitor prioritization. The combined QSAR, explainability, external corroboration, and docking strategy supports shortlist generation for experimental follow-up. Full article

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569, $Q_{LOO}^2$ = 0.7244) was validated by an external set of 8 experimental compounds ($R_{ext}^2$ = 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale. Full article

Understanding the adsorption of organic pollutants onto microplastics in aqueous environments is crucial for assessing their environmental behavior and ecological risks. Herein, we used density functional theory (DFT) computations to simulate the aqueous adsorption of 54 organic compounds onto three representative microplastics, namely polyethylene (PE), polyoxymethylene (POM), and polyvinyl alcohol (PVA). Afterwards, based on theoretical molecular structural descriptors, we developed six quantitative structure activity relationship (QSAR) models based on datasets of 43 and 54 organic compounds, respectively. The results demonstrated that the oxygen-containing POM and PVA microplastics exhibited weaker adsorption in the aqueous phase compared to that in the gas phase. Furthermore, it revealed that the electron-rich atoms, van der Waals volumes and molecular polarizability exert substantial effects on the adsorption process on microplastics in water. These robust QSAR models can enable the prediction of adsorption energies for various organic pollutants on microplastics, which can offer a rapid approach for generating adsorption data. Moreover, the insights into adsorption mechanisms can provide a theoretical basis for designing modified or alternative plastics with lower environmental risks. Full article

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