Journal of Computer-Aided Molecular Design最新文献

Despite the widespread use of Grossamide-containing plants in traditional medicine and its documented anti-inflammatory and metabolic regulatory properties, this lignanamide's potential as an antidiabetic agent remains unexplored. Current α-glucosidase inhibitors like acarbose suffer from poor patient compliance due to severe gastrointestinal side effects, creating an urgent need for better-tolerated alternatives. This study investigated whether Grossamide’s unique structural features and established bioactivities could translate into clinically relevant carbohydrase inhibition. Through integrated computational and experimental approaches, we demonstrate that Grossamide exhibits potent dual inhibition of α-amylase (IC₅₀: 44.4 ± 5 μM) and α-glucosidase (IC₅₀: 72 ± 5 μM), showing 50% and 33% lower IC₅₀ values than acarbose (89 and 108 μM, respectively) and comparing favorably to natural inhibitors like quercetin (> 200 μM) while approaching potencies of semi-synthetic derivatives, though not reaching synthetic drug levels (0.2–1 μM). Molecular docking revealed distinct binding modes for each enzyme, with preferential α-amylase engagement potentially reducing side effects associated with excessive α-glucosidase inhibition. Extensive molecular dynamics simulations (100 ns) confirmed binding stability and identified a persistent hydrogen bond network with GLN63 (91% occupancy) as critical for α-amylase inhibition, while α-glucosidase binding involved dynamic interactions across multiple subsites. MM/GBSA calculations revealed strong binding affinities driven predominantly by van der Waals forces, contrasting with the electrostatic-dependent binding of current clinical inhibitors. Comprehensive ADMET profiling predicted acceptable drug-likeness despite the compound's large size, with favorable safety parameters supporting therapeutic development. These findings establish Grossamide as a promising scaffold for developing dual-action antidiabetic agents and demonstrate how computational drug design can identify new therapeutic applications for known natural products, potentially accelerating the drug discovery timeline by repurposing compounds with established safety profiles.

The overexpression or activation of C-terminal Src kinase (CSK) has been recognized as a pivotal factor in the progression of hepatocellular carcinoma (HCC), positioning CSK as a promising therapeutic target. Despite this potential, no CSK-specific inhibitors have been developed for HCC treatment to date. Addressing this gap, our study established a robust virtual screening protocol that integrates energy-based screening techniques with machine learning methodologies. Through this systematic approach, we identified a novel compound, 6, exhibiting potent CSK inhibitory activity, as evidenced by an IC₅₀ value of 675 nM in a homogeneous time-resolved fluorescence (HTRF) bioassay. Notably, this compound demonstrated significant growth inhibition in Huh-7 and Huh-6 cell lines, along with the suppression of clone formation. To elucidate the underlying mechanism, we conducted molecular dynamics simulations, which revealed critical binding interactions between compound 6 and CSK. Specifically, residues Phe333 and Met269 were found to play essential roles in mediating these interactions, providing valuable insights into the compound’s mode of action.

Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a–7j), incorporating pyridine and thiophene scaffolds, were synthesized and structurally characterized using FTIR, ¹H-NMR, LC–MS, and elemental analysis. In vitro, antimicrobial screening demonstrated that compounds 7c and 7j displayed consistent and potent activity across Gram-positive and Gram-negative bacterial strains and fungal pathogens, with compound 7c achieving MICs as low as 25 µg/mL. Compound 7c exhibited significant antitubercular activity with 96% inhibition at 25 µg/mL against Mycobacterium tuberculosis H37Rv. A deep learning-based QSAR model, developed using a fully connected feedforward neural network trained on molecular descriptors (MolWt, LogP, TPSA, H-bond donors/acceptors, etc.), yielded predicted pMIC values closely matching experimental trends. SHAP analysis confirmed the multivariate contribution of key descriptors, validating the model’s interpretability despite dataset constraints. SwissADME-based pharmacokinetic profiling confirmed high gastrointestinal absorption, low PAINS alerts, and compliance with Lipinski and Veber rules for drug-likeness. Compounds 7c and 7j demonstrated balanced lipophilicity, low skin permeability, and favourable ADMET characteristics, aligning with their firm biological profiles. Molecular docking showed strong binding affinities for 7c (− 11.55 kcal/mol with CYP51) and 7j (− 9.97 kcal/mol with InhA), with multiple hydrogen bonds and hydrophobic interactions at catalytically relevant sites. These interactions were consistent with observed antimicrobial profiles. These docking predictions were validated by 200 ns molecular dynamics simulations, which confirmed the structural stability of 7c and 7j in complex with CYP51, InhA, PBP2a, and DNA Gyrase B. RMSD and RMSF trajectories, indicated stable ligand retention and minimized flexibility at the binding interface, particularly for 7c with CYP51 and InhA, and for 7j with DNA Gyrase B. These results support 7c and 7j as promising lead candidates with dual antimicrobial potential, favourable drug-like properties, and broad-spectrum activity profiles.

The widespread use of pesticides such as deltamethrin (a pyrethroid) and acetamiprid (a neonicotinoid) has sparked concerns regarding their effects on human health, particularly their potential role in carcinogenesis. This study investigated the cytotoxic, molecular, and functional effects of these pesticides, individually and in combination, on the MDA-MB-231 triple-negative breast cancer (TNBC) cell line. This model was chosen to specifically investigate estrogen recpetor (ER)-independent mechanisms due to its expression of targets such as aryl hydrocarbon receptor (AhR), peroxisome proliferator-activated receptor gamma (PPARγ), and G protein-coupled estrogen receptor (GPER); however, it does not reflect normal mammary cell responses. Cytotoxicity was assessed via XTT assays, migration was analyzed using wound-healing assays, and gene expression changes in AhR, PPARγ, and Caspase-3 were measured using RT-qPCR. Molecular docking was performed to predict pesticide-protein interactions, and in silico toxicity assessments using ProTox-II supplemented the in vitro results by predicting toxicity profiles relevant to public health. Both pesticides exhibited dose-dependent cytotoxicity, and their combination produced an additive effect on cell viability. Importantly, suppression of cell migration and downregulation of AhR and PPARγ expression reflected toxic stress responses at high pesticide concentrations, rather than therapeutic or anti-cancer potential. While apoptosis-related gene expression (Caspase-3) was increased, this effect did not reach statistical significance. Molecular docking supported strong interactions with key pathways related to xenobiotic metabolism and apoptosis. These findings emphasize that, at high and non-environmentally relevant concentrations, deltamethrin and acetamiprid induce additive cytotoxic effects and disrupt molecular processes in a mechanistic cancer model. The results highlight the need for further investigation using normal cell systems and environmentally relevant exposures to clarify real-world risk and biological mechanisms, and should not be interpreted as evidence of therapeutic activity. This study underscores the mechanistic relevance of pesticide exposure in environmental toxicology rather than any potential therapeutic application.

Fluoxastrobin (FLUO) is a fungicide from strobilurin family used widely worldwide. The use of FLUO pesticide is on the rise and this phenomenon is accompanied by a series of concerns such as endocrine disruption. In order to determine the potential toxic effects of FLUO, cell culture, gene expression and molecular docking assays were conducted as it is crucial to determine the interaction between chemicals and nuclear receptors in order to estimate and understand the impact of the chemical. This study analyzed the quantum properties of FLUO at the molecular quantum mechanical level using Density Functional Theory (DFT) with the B3LYP/6-311 + + G(d, p) and cc-pVDZ basis sets including the HOMO-LUMO energy gap, chemical reactivity descriptors, molecular electrostatic potential (MEP) surface calculation. In order to investigate molecular characteristics, topological (AIM, RDG) and Natural Bonding Orbitals (NBO) investigations were conducted. Molecular docking studies were performed with the title compound in the active sites of the proteins selected because of their role in xenobiotic metabolism. The docking result was determined to be a significant factor in bioactivity, a finding that is corroborated by the cytotoxic analysis of the FLUO compound. Density Functional Theory (DFT) computations are used to support molecular docking analysis. Toxicity of FLUO was tested on MDA-MB-231 cells using XTT and wound healing assays. IC50 value of FLUO was determined as 6,9 µg/ml. The impact of FLUO exposure at molecular level was assessed using qRT-PCR by determining the expression levels of PPARy, AhR and PXR genes where no statistically significant change was found.

Organohalide-respiring bacteria encoding reductive dehalogenases have shown substantial potential for bioremediation of organohalogen-contaminated environments. However, limited reactivity towards emerging pollutants, particularly fluorinated organics, constrains the broader application of these enzymes. To elucidate the molecular basis of this limitation, we investigated ligand-recognition mechanisms of the chlorinated-ethene dechlorinase PceA using molecular dynamics simulations. We find that tetrachlorinated ligands are stably accommodated in the binding pocket, whereas tetrafluorinated ligands can form hydrogen bonds with polar residues and are preferentially stabilised in a sub-pocket away from the catalytic site. Binding free-energy analyses indicate that van der Waals interactions and nonpolar solvation are the primary driving forces for association, favouring higher degrees of chlorination and longer carbon chains, and are facilitated by multiple aromatic residues. By contrast, polar solvation consistently opposes binding, with Arg305 acting as an antagonistic residue. Notably, polar solvation becomes more favourable with increasing fluorination for halogenated methanes and ethenes. The present study can provide insight for the relationship between binding free energy and ligands with various level of fluorination/chlorination and carbon chain length. The identified driving energy for ligand binding can be useful for understanding the limitations of reductive dehalogenase towards organofluorinated compounds.

Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this principle using deep learning to engineer monoclonal antibodies (mAbs) targeting immune checkpoints PD-1 and LAG-3. These two checkpoints are targeted synergistically in combination immunotherapy to minimize cancer cell evasion. From the established antibodies, the best set was selected based on their clinical validation. These served as templates to improve binding affinity and therapeutic potential in the heterogeneous tumor microenvironment. To guide antibody design, we formulated inverse modeling pipeline using message passing graph neural network for protein sequence design given a fixed backbone structure. This led to the prediction of functionally viable substitutions at the receptor-antibody interface. Resulting variant models were filtered based on physicochemical accuracy, evolutionary feasibility, empirical validation, geometric complementarity and machine learning guided mutation prediction, ensuring structural integrity and enhanced performance. In addition, thermostability and immunogenicity analyses of the filtered ones were carried out. Ultimately, the top candidates were subjected to molecular dynamic (MD) simulations leading to post simulation trajectory analysis including stability, interaction and energy decomposition analysis. After a robust computational evaluation, seven variants exhibited improved network stability and superior binding as compared to their respective references. Moreover, we have also added negative control to reinforce the novelty and importance of our framework. Our results establish a robust and scalable framework to design ICIs and underscores potential leads having improved binding, concertedly targeting PD-1 and LAG-3, paving the path for next-generation immunotherapy.

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Mycobacterium tuberculosis (Mtb) continues to be one of the major contributors to the global burden of infectious diseases. Many drugs used in the current treatment regime have fallen prey to the puzzling phenomenon of antimicrobial resistance. Despite various attempts, few recent drugs have been developed against the bacterium (Sharma A, Vadodariya PK, Vaddoriya VN, Dhameliya TM (2025) Comprehensive updates on antitubercular endeavors identified in 2023. Synlett 36:2393–2410. https://doi.org/10.1055/a-2595-8032; Patel KI, Saha N, Dhameliya TM, Chakraborti AK (2025) Recent advancements in the quest of Benzazoles as anti-Mycobacterium tuberculosis agents. Bioorg Chem 155:108093. https://doi.org/10.1016/j.bioorg.2024.108093; Dhameliya TM, Bhakhar KA, Gajjar ND, Patel KA, Devani AA, Hirani RV (2022) Recent advancements and developments in search of anti-tuberculosis agents: a quinquennial update and future directions. J Mol Struct 1248:131473. https://doi.org/10.1016/j.molstruc.2021.131473). The proteins involved in Mtb’s fatty acid synthase II (FAS-II) system are suitable drug targets. Many of the enzymes in this pathway, like β-ketoacyl-acyl carrier protein (KasA), 3-oxoacyl-[acyl-carrier-protein] synthase II (KasB) and β-ketoacyl-[acyl-carrier-protein] synthase III (FabH), are indispensable to Mtb but have no counterpart in humans. Here, we present an integrative approach starting with the curation of site specific dataset, exploratory data analysis with multiple machine learning models, virtual screening of compound library with hypertuned artificial neural networks (ANN) having hidden layers, molecular docking studies and in vitro validation to target some of the key elements involved in the mycolic acid chain elongation step during biosynthesis. By employing a multi-target paradigm, which is more resilient to antibiotic resistance due to simultaneous effect on multiple targets, we have targeted the above key synthases in the FAS-II pathway and validated the identified compounds’ potential as anti-mycobacterial agents using in vitro biological evaluation. Molecular dynamics (MD) simulations further corroborated the potential of active compounds across targets. These molecules present new starting scaffolds, having inhibitory activities of up to 90% with respect to the positive control, for further improvement in terms of their potency as FAS-II pathway inhibitors with the help of medicinal chemistry efforts.

β-Cyclodextrin (β-CD) is a widely used host molecule in supramolecular chemistry, pharmaceutical formulations, and chiral recognition. However, its conformational flexibility, critical to the thermodynamics and geometry of its inclusion complexes, is often underrepresented in computational modeling. In this study, we present a large-scale conformational analysis of β-CD to support accurate modeling of its inclusion complexes. A total of 437 β-CD conformations were extracted from 293 Cambridge Structural Database entries and optimized using B3LYP-D3/6-31G(d,p) both in vacuo and with an implicit water PCM model. Hierarchical clustering of Gibbs free energies revealed 18 major conformational clusters (in vacuo) and 17 (PCM) spanning approximately 40 kcal/mol. Simulated annealing and quench dynamics from the most and least stable geometries yielded low-energy conformers, four of which converged to a new global minimum approximately 9 kcal/mol below any experimental structure. A moderate correlation (Spearman r ≈ 0.60) between vacuum and solvated Gibbs free energy values indicates solvent-dependent reordering. Guest molecule descriptors were also analyzed to explore host–guest structural correlations. Cartesian coordinates for 19 representative β-CD conformers are provided as a ready-to-use resource for molecular modeling, ensemble docking and free energy studies. These findings highlight the importance of conformational selection in accurately modeling β-CD inclusion complexes and may enhance binding affinity predictions, with direct application in drug delivery and chiral recognition.

Drug repositioning (DR) is a highly promising research strategy aimed at discovering new therapeutic indications for existing drugs. Current computational DR methods have become effective tools for uncovering drug-disease associations, yet they suffer from three critical limitations: most models can only extract either local or global embeddings of node features, traditional methods often construct shallow networks due to the vanishing gradient problem, making it difficult to capture the complex multi-level relationships between drugs and diseases, and they struggle to mine meaningful information from small-scale negative samples. To overcome these limitations, we propose an innovative method named GADRC, which employs a synergistic architecture of graph convolutional networks and graph attention networks to simultaneously capture local structural features of drug molecules and global pathway features of diseases for the first time. Additionally, we introduce a biologically interpretable deep residual network, whose cross-layer identity connection mechanism effectively addresses the depth degradation problem in traditional graph neural networks, enabling the model to stably learn multi-level interactions between drug targets and disease markers. Finally, we develop a feature-guided undersampling strategy combined with a weighted cross-entropy loss function, which constructs biologically similar subgroups through positive sample feature clustering and dynamically selects hard negative samples with weighted importance, significantly improving the utilization efficiency of negative samples. Experimental results on three benchmark datasets demonstrate that GADRC consistently outperforms most methods in DR tasks. Moreover, case and molecular docking studies on Alzheimer’s disease and breast cancer further validate its effectiveness and provide new insights into GADRC’s ability to identify novel drug-disease associations.