Journal of Computer-Aided Molecular Design最新文献

This work presents the synthesis and detailed structural elucidation of a new cis-dioxotungsten(VI) complex, featuring a novel hexacoordinate geometry. The complex was formed using a custom-synthesized Schiff base ligand, N-(4′-acetylidene-3′-methyl-1′-phenyl-2′-pyrazolin-5′-one)-4-amino-2,3-dimethyl-1-phenyl-3-pyrazolin-5-one (Hampph-aap), designed to provide specific coordination pockets for metal binding. The reaction of this ligand with the precursor complex, tetraisothiocyanatodioxotungstate(VI) [WO₂(NCS)₄]²⁻, proceeded in a 1:1 molar ratio, yielding a diamagnetic product with the empirical formula [WO₂(L)(NCS)]. Comprehensive spectroscopic analysis, including FT-IR, UV–Vis, multinuclear NMR, and high-resolution mass spectrometry, confirmed the successful formation of the complex. The data collectively demonstrate that the Hampph-aap ligand behaves as a monobasic tridentate chelator, binding to the tungsten center after deprotonation. To move beyond experimental characterization and gain deeper insight into the molecular framework, Density Functional Theory (DFT) calculations at the WB97XD/SDD level with SMD solvation were employed. The computational study involved full geometry optimization and subsequent simulation of vibrational, electronic, and NMR spectra. The theoretically optimized structure is in excellent agreement with the experimental data, robustly confirming an octahedral geometry around the central tungsten atom. A key finding from both experimental and computational analyses is the unambiguous identification of the thiocyanate co-ligand binding through its nitrogen atom (N-bonded isothiocyanate) based on comparative theoretical analysis between N- and S-coordinated systems. Beyond structure, DFT analysis revealed significant nonlinear optical (NLO) potential and proposed catalytic relevance in epoxidation, supported by DOS and preliminary docking studies. This combined experimental and theoretical approach provides a conclusive and multi-faceted understanding of the complex's structural, electronic, and applied properties.

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Dipeptidyl peptidase-4 (DPP-4) remains an attractive target for the development of orally active antidiabetic agents. Building on the reported pyrazole, isatin, and triazole pharmacophores, we designed, synthesized, and evaluated two series of DPP-4 inhibitors: pyrazole-isatin hybrids (Sa–Sg) obtained by SN2 O-alkylation and pyrazole-triazole-isatin hybrids (7a–7f) constructed via CuAAC “click” chemistry. All final compounds and key intermediates were fully characterized by IR, ¹H/¹³C NMR, HPLC, and MS. In vitro ELISA assays at 50, 75, and 100 nM demonstrated potent DPP-4 inhibition across both series. Within the triazole-linked set, compound 7e showed 87.95% inhibition with an IC₅₀ of 1.56 nM, while 7d and 7c also displayed low-nanomolar IC₅₀ values, comparable to the reference drugs sitagliptin and teneligliptin under identical conditions. Structure-based studies against human DPP-4 (PDB: 3VJK) using induced-fit docking and MM-GBSA rationalized the observed SAR, revealing recurrent occupation of the S1/S2/S1′/S2′ subsites and key interactions with TYR666, PHE357, ARG125, SER630, and ASN710. Molecular dynamics simulations (200 ns) of the 7c, 7d and 7e-bound complexes supported persistent binding and protein stability, with 7e exhibiting the most favorable dynamic interaction profile. QikProp-based ADMET predictions indicated generally drug-like properties, high predicted oral absorption, and good compliance with Lipinski and Jorgensen rules. Overall, the pyrazole-triazole-isatin chemotype, particularly analogues 7d and 7e, emerges as a promising lead framework for next-generation, orally available DPP-4 inhibitors, meriting further optimization for selectivity, safety, and in vivo antidiabetic efficacy.

Medicinal plants are an important source of bioactive secondary metabolites that are responsible for the development of new drugs. The main aim of this study was to explore Pistacia integerrima J. L. Stewart ex Brandis phytochemically and biologically explore P. integerrima. The defatted methanolic extract of P. integerrima galls was subjected to column chromatography, which yielded six flavonoids including 3,5,7,4/-tetrahydroxy-flavanone (1), naringenin (2), 3,5,4/-trihydroxy,7-methoxy-flavanone (3), sakuranetin (4), spinacetin (5), and patuletin (6). The defatted extract and the isolated compound (1–6) were assessed for in- vitro xanthine oxidase (XO). The samples to be tested were applied at a concentration of 0.5 mM and demonstrated a variable degree of XO inhibitory potential. The maximum inhibitory effect was observed for compound 6 (93.09%), followed by compounds 5 (89.02%) and 3 (87.92%). Six flavonoids from P. integerrima galls showed favorable drug-likeness, good gastrointestinal (GI) absorption (except for compound 6), and safe oral toxicity profiles. Docking and in vitro assays identified compounds 3, 5, and 6 as potent XO inhibitors that outperformed allopurinol. Density functional theory (DFT) analysis revealed that compound 3 was stable but less reactive, whereas compounds 5 and 6 were more reactive, with strong electrophilic properties. Furthermore, MD simulations confirmed the stable binding of these three compounds within the XO active site, with compound 6 demonstrating the highest interactions and structural stability. In conclusion, P. integerrima flavonoids, particularly compound 6, are significant XO inhibitors that may be used to treat hyperuricemia and hypoxic-ischemic encephalopathy (HIE).

Lung cancer remains one of the deadliest cancers worldwide, largely due to late-stage diagnosis and the complex, often asymptomatic progression of the disease. The study presents a new noise-aware Computer-Aided Diagnosis (CAD) framework for lung cancer detection in CT scans, addressing the critical challenge of image noise that can obscure vital diagnostic details. Thus, the proposed work uses a multilayer perceptron-based classifier that uses texture descriptors from the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP), integrates Inception V3 for feature extraction, and introduces a high-level adaptive Gaussian filter with Multi-spatial Channel Attention (MSCA) convolutional segmentation. Finally, classification was achieved via a multilayer perceptron (MLP) using a novel Adaptive Osprey Optimization Algorithm (AOOA). This architecture enables effective feature learning, segmentation, and classification through modular integration of CNN, attention, and statistical texture extraction. The experimental results on the IQ-OTH/NCCD dataset show a classification accuracy (0.9894), specificity (0.9917), sensitivity (0.9846), and AUC metrics. This framework holds strong potential for real-world clinical integration, offering improved early diagnosis and supporting radiologists in lung cancer assessment.

Promoters are key DNA elements that regulate bacterial gene expression, yet most existing computational methods demonstrate limited effectiveness in predicting promoters across diverse bacterial species. Here, we propose PBP_ICBA, a deep learning model featuring a dual-path architecture that integrates two-dimensional convolution and improved Convolutional Block Attention Module for accurate species-specific bacterial promoter identification. The model employs a comprehensive encoding scheme combining one-hot encoding, Nucleotide Chemical Property C2, and ESM-2 representations. Evaluation on 13 species-specific bacterial promoter datasets shows that PBP_ICBA achieves superior performance in 11 species. This study provides a robust framework for species-specific bacterial promoter prediction and enhances our understanding of transcriptional regulatory mechanisms. Research data is available in this public repository: https://github.com/liuchang-chun /PBP_ICBAA.

The papain-like protease of SARS-CoV-2 (PLpro2) is integral to viral polyprotein cleavage and the modulation of host immune responses, positioning it as a critical target for antiviral drug development. Here, we elucidate the molecular mechanisms governing the noncovalent inhibition of PLpro2 through a comprehensive computational approach, including molecular docking, extensive molecular dynamics (MD) simulations, binding free energy calculations (MM/GBSA and SIE), principal component and free energy landscape (PCA/FEL) analyses, and protein–ligand interaction fingerprinting (ProLIF). We assessed a structurally diverse set of noncovalent inhibitors for their capacity to induce conformational rearrangements and stabilize key structural motifs of PLpro2, with particular emphasis on the BL2 loop. Notably, XR3 and A19 exhibited superior experimental and predicted binding affinities, which can be attributed to favorable contacts with essential residues Tyr268 and Gln269, the attenuation of loop dynamics, and the stabilization of energetically favorable conformational states. By contrast, less potent inhibitors were associated with increased conformational heterogeneity, fragmented free energy landscapes, and diminished interactions with critical loop residues. Therefore, our integrative analysis delineates the structural and energetic determinants underpinning noncovalent PLpro2 inhibition, underscoring the central roles of loop immobilization and π-stacking interactions in the rational design of next-generation PLpro2 inhibitors.

Natural products have crucial relevance both in traditional medicine as well as in modern drug discovery. Indeed, they inspire currently developed drugs, emphasizing the importance of biodiversity and sustainability. Alzheimer’s disease (AD), a complex neurodegenerative disorder marked by amyloid plaques and neurofibrillary tangles, involves dysregulation of molecular pathways including increased cholinesterases and monoamine oxidase-B (MAO-B) activities, with enzyme inhibition remaining a key therapeutic strategy. This study investigates pistagremic acid, a triterpene from Pistacia chinensis subsp. integerrima and its inhibitory effects on such crucial enzymes implicated in AD. The compound showed moderate inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in vitro with selectivity for AChE, while a potent inhibition of MAO-B was noted, indicating potential neuroprotective effects by reducing oxidative stress. Molecular docking showed interactions with key enzyme residues, and off targets were studied with a ligand-based approach. The findings support its multi-target therapeutic potential, but also prompt future studies exploring selectivity profile.

Background

Breast cancer (BC), the most aggressive cancer in women, continues to exhibit serious health concerns globally. However, the existing limitations in current therapeutics demand novel, broad-spectrum and cost-effective treatment. Herbal medicines are currently used as effective anticancer treatments, comprising specific plant-derived bioactive compounds. Jervine, a steroidal alkaloid, has proven to have anticancer activities; hence, its therapeutic mechanism in contributing to BC tumorigenesis remains incompletely elucidated.

Objective

This study investigates the molecular mechanism of action of jervine’s anticancer efficacy against BC via in silico and in vitro analyses.

Methods

In silico analysis, including network pharmacology, pharmacokinetics, molecular docking, and molecular dynamics simulations, was performed to explore jervine’s therapeutic potential against BC targets. Further,an invitro analysis was evaluated for its cytotoxic effects using MDA-MB-231 triple-negative breast cancer (TNBC) cells, using the MTT assay, followed by gene expression analysis via RT-PCR.

Results

The combined findings from network pharmacology, molecular docking, and molecular simulation analyses clearly demonstrated that jervine possesses favourable drug-like properties and forms stable interactions with multiple Wnt signalling targets (WNT5A, FZD5, LRP6), as well as key modulators from other pathways, including BMP4 (a member of the TGF-β family) and SHH (a central component of Hedgehog signalling), all of which are associated with breast cancer. In vitro analysis of MDA-MB-231 cells using the MTT assay showed that jervine exhibited significant cytotoxic effects, with IC₅₀ values of 18.55 μM at 24 h and 13.23 μM at 48 h. Further, gene expression analysis particularly emphasized that jervine significantly controlled the dysregulated WNT signalling targeted genes (Wnt 5a, DVL2, FZD5, LRP6) and including SHH, BMP4 genes mRNA expression levels and thereby inhibited the proliferation of BC cells. Further, these in vitro results coincide with the findings of the computational studies.

Graphical abstract

The graphical abstract represents a research workflow that explores the anticancer effects of the compound jervine on breast cancer through various in silico and in vitro processes, including computational analysis, biological evaluation, and mechanistic insights. The illustration highlights the molecular interactions from in silico and in vitro studies of jervine's therapeutic potential in breast cancer (BC).

Massively-multitask regression models (MMRMs) have revolutionized activity prediction for drug discovery. MMRMs trained on millions of compounds and many thousands of assays can predict bioactivity with accuracy comparable to 4-concentration IC₅₀ experiments. This report compares six MMRMs: pQSAR, Alchemite, MT-DNN, MetaNN, Macau and IMC. Models were trained by experts in each method, on identical sets of 159 kinase and 4276 diverse ChEMBL assays, employing realistically novel training/test set splits. Results were compared both qualitatively and with statistical rigor. Our use-case is imputing full bioactivity profiles for the very sparse compound collections on which the models were trained. MMRMs performed much better than the single-task random forest regression (ST-RFR) model. Five MMRMs train all models simultaneously, so must leave out test-set measurements from all assays to avoid leakage (here 25% of data), whereas one method trains models one-at-a-time, so only holds out test data for that assay (< 1% of data). Thus, all algorithms were compared both using 75/25 splits, and when possible, 99 + / < 1 splits. Many MMRM evaluations achieved similar accuracy when tested on the same split. However, when evaluated on 75/25 splits, all MMRMs performed much worse than when evaluated on 99 + / < 1% splits. Thus, while many MMRMs produce comparable final production models (trained on all the data), models that require 75/25 splits greatly underestimate the accuracy of the final models. While outstanding for imputations, MMRMs proved little better than ST-RFR for compounds very unlike the training collection. Thus, MMRMs are best for hit-finding, off-target, promiscuity, MoA, polypharmacology or drug-repurposing within the training collection. Since accuracy is not a deciding factor, other pros and cons of each method are also described.

The monoamine oxidase (MAO) enzymes are mitochondrial flavoenzymes that catalyse the oxidative deamination of neurotransmitter amines such as serotonin, norepinephrine and dopamine. Inhibitors of the MAOs are well-known antidepressant and antiparkinsonian agents, and act by reducing MAO-mediated metabolism of neurotransmitters in the brain. The present study attempted to identify compounds that inhibit the MAOs by virtual screening of existing drugs listed in the DrugBank using the Discovery Studio life science software. To identify the combinations of docking and scoring functions that most accurately identify known MAO inhibitors, the enrichment factor (EF^10%) and area under the receiver operating characteristic curve (ROC-AUC) were evaluated. As a third validation metric, ligands that have been complexed with the MAOs were redocked and the root mean square deviation (RMSD) from the co-crystallized orientation was measured. The LibDock/LigScore 2 combination yielded the best results for both MAO-A (EF^10%: 5.20, ROC-AUC: 0.82) and MAO-B (EF^10%: 7.47, ROC-AUC: 0.89). Among the top 100 hits, ten compounds were selected and evaluated as in vitro inhibitors of human MAO. Guanabenz (IC₅₀ = 3.46 µM) and proflavine (IC₅₀ = 0.223 µM) were found to be the most potent MAO-A inhibitors. These compounds also inhibited MAO-B with IC₅₀ values of 8.49 and 34.3 µM, respectively. Kinetic analysis showed a competitive mode of MAO-A inhibition for guanabenz (K_i = 0.16 µM) and proflavine (K_i = 0.066 µM). These results show that the validated virtual screening protocol is a useful tool to aid in the discovery of MAO inhibitors.

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