SAR and QSAR in Environmental Research最新文献

Childhood obesity is a severe global epidemic, and emerging evidence suggests environmental pollutants like polystyrene microplastics (PS-MPs) may disrupt metabolic homoeostasis though mechanistic insights remain limited. This study integrated cross-species transcriptomics (from zebrafish and human adipose datasets), network toxicology, machine learning, and molecular docking to explore this link. We identified 40 overlapping genes between childhood obesity related DEGs and PS-MPs related genes, enriched in lipid metabolic pathways such as cholesterol homoeostasis and insulin signalling. Topological and machine-learning analyses highlighted hub genes, which showed strong diagnostic accuracy. Molecular docking further revealed stable binding (energy < -5.0 kcal/mol) between PS-MPs and key targets (APOB、BUB1、CDC20 and PPARGC1A). Our integrative analysis suggests that PS-MPs may act as an environmental trigger that could disrupt conserved lipid and metabolic homoeostasis by targeting key hub genes (APOB、BUB1、CDC20 and PPARGC1A). These findings provide a novel molecular hypothesis linking PS-MPs exposure to childhood obesity and support precautionary measures.

This study aimed to investigate the interaction potential of sponge metabolites towards signal transducer and activator of transcription 4 (STAT4), one of the potential targets related with rheumatic heart disease (RHD). Out of 100 sponge molecules screened, Halenaquinone demonstrated as potential inhibitor of STAT4 by modelling. Molecular docking revealed that Halenaquinone exhibited strong binding affinity with STAT4 (-10.2 kcal/mol) and favourable interactions, surpassing the binding of the reference drug Prednisolone-NR3C1complex. The interaction between STAT4 and Halenaquinone stabilized through hydrogen bonding and hydrophobic contacts involving several key residues, supporting its possible inhibitory mechanism. Molecular dynamics simulation studies indicated the stability of the STAT4-Halenaquinone complex, with some flexibility observed in the loop and terminal regions. Free energy decomposition revealed that van der Waals and electrostatic interaction were the main contributors to binding. Furthermore, principal component analysis, dynamic cross correlation and free energy landscape analyses suggested that Halenaquinone binding enhances the conformational adaptability of STAT4, consistent with its role as a potential mediator. The present study provides a computational model of STAT4 as a promising target in RHD and suggests Halenaquinone is a potential marine-sponge-derived compound with therapeutic potential.

AI-ML approaches emerged as transformative technologies in cancer drug discovery by accelerating the target identification and lead optimization. EGFR and CDK2 are crucial targets in cancer therapy which involved in cancer proliferation and metastasis. However, existing inhibitors face challenges like resistance, toxicity and poor pharmacokinetics. Phthalimide scaffolds possess dual or multi-target efficacy which serve a promising drug. This study explores phthalimide-based dual EGFR and CDK2 inhibitors addressing limitations of current anticancer agents. Initially, the 3D-QSAR model was developed and validated using 58 phthalimide derivatives (r² = 0.998, Q² = 0.852 and MAE = 0.299). The novel 3886 phthalimide derivatives were generated using the MolOpt server by bioisosteric replacements and screened over the 3D-QSAR model. Notably, 80 novel derivatives demonstrated exceptional anticancer potency (IC₅₀ < 10 nM). Molecular docking, binding free energy, MM-PBSA and MM-GBSA confirmed strong binding affinities, stability and dual action of novel compounds (1472, 1486 and 1458) with EGFR and CDK2. DFT analysis revealed favourable electronic properties and supporting their reactivity. AI-driven ADMET predictions confirmed their drug-like characteristics. This study highlights the AI-ML driven methodologies in the discovery of novel phthalimide derivatives (1472, 1486 and 1458) as potent anticancer agents (IC₅₀ = 3.6, 6.2 and 7.4 nM).

Tryptophan catabolism through the kynurenine pathway produces the oncometabolite kynurenine, which is strongly implicated in cancers such as triple-negative breast cancer (TNBC). The enzymes indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-dioxygenase (TDO) drive this pathway and promote an immunosuppressive tumour microenvironment, making them an attractive therapeutic target. However, no approved drug currently inhibits both enzymes simultaneously. In this study, we employed a machine learning (ML)-driven virtual screening pipeline to identify potent dual IDO1 and TDO inhibitors. Initially, an in-house ML classification model was developed using IC₅₀ values from 1,037 distinct dual inhibitors sourced from the ChEMBL and BindingDB databases. Among the various models evaluated, the eXtreme Gradient Boosting with Random Forest (XGBRF) classifier achieved the highest performance (95% accuracy) and was selected to screen the MEGxp database. Subsequent molecular docking, MM-GBSA calculations, rescoring, and ADMET profiling identified two promising candidates, NP000319 and NP003833. Both compounds also showed predicted anticancer potential against MDA-MB-231 TNBC cells. Furthermore, the stability of the protein-ligand complexes was confirmed through 100 ns molecular dynamics simulations. Overall, the study highlights the value of ML-driven dual-inhibition strategies and provides strong leads for future experimental validation and potential therapeutic development for TNBC.

Human epidermal growth factor receptor (EGFR) has been approved as a well-established druggable target of lung carcinoma. The binding peptide segments of both substrate and inhibitory proteins contain a phosphorylatable tandem YY motif but interact with EGFR kinase domain in different manners. Here, the two tandem substrate Y₀Y₊₁ and inhibitor Y₊₁Y₊₂ motifs were aligned to define a new pseudo triad tyrosine Y₀Y₊₁Y₊₂ (PtriY) motif, in which the Y₀, Y₊₁ and Y₊₂ residues bind to catalytic, priming and priming pockets on EGFR kinase domain surface, respectively. Here, we examined the effects of different PtriY phosphorylation codes on EGFR binding and created a systematic single-point substitution-binding energy change profile of its N- and C-terminal extensions, which was then used to develop and validate quantitative structure-activity relationship (QSAR) models. The best model was utilized to guide rational peptidic inhibitor design, from which more than 40 promising hits were selected to perform affinity and/or kinase assays. The QSAR-designed PH2[Y₀pY₊₁pY₊₂] peptide (ENGHY₀pY₊₁pY₊₂AL) was identified to have the strongest binding potency (K_d = 0.26 ± 0.07 μM) and the highest inhibitory activity (IC₅₀ = 5.8 ± 0.9 nM), which consists of an amphiphilic N-terminal extension, a double-phosphorylated PtriYmotif and hydrophobic C-terminal extension.

This article presents the results of computational screening of approved drug compounds to find new inhibitors of acetylcholinesterase (AChE), an enzyme that plays a key role in the regulation of neurotransmission and cognitive functions. Using molecular docking and quantum chemical postprocessing methods, the authors conducted a virtual screening of a library of 2909 drug compounds approved for clinical use from two ZINC database libraries. The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). As a result of the docking, 211 of the most promising ligands were selected for calculating their enthalpy of binding to AChE using quantum chemical calculations. Based on the analysis of the free energy of binding estimated by docking score and the enthalpy of binding calculated using the quantum-chemical PM7 method with the COSMO solvent model, 16 of the most promising candidates for the role of AChE inhibitors were identified. Notable candidates include Pixantrone, Guanfacine and Hydroxystilbamidine. These compounds, although not previously known as AChE inhibitors, represent diverse chemical classes including substituted thiophenes, pyridines, and fused nitrogen-containing heterocycles, showing high potential for treating neurodegenerative diseases such as Alzheimer's disease.

Antioxidant agents that efficiently scavenge reactive oxygen species (ROS) are of great interest in medicinal chemistry for their potential to mitigate oxidative stress-related pathologies. In this work, we developed an interpretable Multiple Linear Regression (MLR) QSAR model using seven molecular descriptors (D/Dr05, MATS2v, MATS8p, Mor24m, L2s, HATS3u, H8m) to predict the free radical scavenging activity of coumarin-based compounds as measured by the IC₅₀ in the DPPH assay. The MLR-QSAR model showed strong goodness-of-fit and robust internal and external validation parameters (r² = 81.04, Q²_LOO = 77.93, Q²_boot = 76.78, r²_ext = 75.38, y_scrambler² = 0.25), supporting its predictive reliability. We applied the model to predict the antiradical potential of a novel set of Warfarin derivatives, a class of molecules historically known for anticoagulant properties but with unexplored antioxidant potential. Experimental in vitro DPPH assays on the seven Warfarin derivatives (WD) revealed a positive correlation (r = 0.63) with the predictions, validating the MLR-QSAR as a screening tool. Furthermore, all WD exhibited significant DPPH radical scavenging activity, demonstrating the chemical antioxidant potential of an anticoagulant-derived scaffold. This dual in silico-in vitro strategy highlights the value of interpretable QSAR models for guiding compound prioritization and structural optimization towards new coumarin-based antioxidants.

Pesticides are widely used in agriculture to enhance crop yield and protect against pests. However, their persistence in soil can lead to long-term environmental contamination and pose health risks to humans and other organisms through indirect exposure via the food chain. In this study, we used in silico approaches like Quantitative Structure-Activity Relationship (QSAR) modelling, Intelligent Consensus Prediction (ICP), and chemical read-across to predict the soil degradation half-lives of various pesticides. Models were established using 2D molecular descriptors, thoroughly validated with the help of training and test sets validation parameters, and conformed to OECD guidelines. The predictive models were applied to the Pesticide Properties Database (PPDB) to demonstrate their utility in screening untested and/or newly synthesized pesticides, considering the domain of applicability. Key structural features associated with degradation were identified, providing valuable insights for the design of biodegradable and environmentally safer pesticides. This work contributes to data gap-filling, regulatory risk assessment, and the prioritization of new or untested pesticides for environmental evaluation.

Cancer remains a major global health challenge, with approximately 18 million new cases reported annually. Existing evidence highlights the PAK1 protein as a critical regulator of cancer progression, making it a promising therapeutic target. The PAK1 protein complexed with dibenzodiazepine was fetched from the PDB with the identifier 4ZLO. The structure was preprocessed through preparation and exposed to pharmacophore hypotheses on the Schrödinger suite programme, indicating key features of RRH. A multi-tiered docking-based screening workflow from the libraries of ZINC and Enamine databases identified five potential bioactive compounds: ZINC952869440, ZINC952869442, ENAMINE558, ENAMINE6304, and ENAMINE8429. The docking and MM/GBSA scores ranked from -5.02 to -8.34 kcal/mol and -46.10 to -50.41 kcal/mol. Remarkably, none of these candidates violated the rules of five, and the Qikprop parameters complied with pharmacokinetic suitability. The DFT analysis revealed energy gap scores ranged from -0.182 to -0.225 eV, indicating favourable electronic properties and stability of the ligands. Furthermore, molecular dynamics (MD) and essential dynamics (ED) studies validated the structural stability of the complexes. The secondary structure analysis indicated stable retention of α-helices and β-strands throughout the simulation. Moreover, the computational investigation identified potential PAK1 inhibitors that warrant further experimental testing and therapeutic development.

The structure of 4-Amino-N-methylphthalimide (4AMP) was investigated through spectroscopic techniques and quantum chemical calculations. Structural parameters were optimized using the DFT-B3LYP/6-311++G(d,p) method in both gas and DMSO phases. Experimental results from powder XRD and reported single crystal XRD showed excellent agreement. The experimental FT-IR and FT-Raman spectra correlated well with theoretical vibrational frequencies, and UV-Vis spectra comparisons further validated the computational findings. Molecular electrostatic potential (MEP), Mulliken and natural charges, and Fukui function analyses highlighted the reactive regions of 4AMP. Natural bond orbital (NBO) analysis revealed stabilization energies of bonding and antibonding orbitals. Hirshfeld surface and fingerprint analyses provided insights into intra and intermolecular interactions. Biological studies indicated that 4AMP exhibited the strongest binding affinity towards the PI3Kα (PIK3CA catalytic subunit) at -6.9 kcal/mol, suggesting significant therapeutic potential. Molecular dynamics simulations over 100 ns have been performed to assess the stability and dynamic behaviour of 4AMP. Cytotoxicity assays demonstrated potent activity against breast cancer cell lines, with IC₅₀ values of 16.89 μg/mL (MCF-7) and 19.53 μg/mL (MDA-MB-231). These findings suggest that 4AMP possesses promising anticancer activity, combining favourable structural, spectroscopic, and biological characteristics, making it a potential candidate for targeted breast cancer therapy.