Journal of Computer-Aided Molecular Design最新文献

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.

Aptamers are short oligonucleotides capable of binding to various molecular targets with high affinity and specificity. These short sequences are conventionally selected through the systematic evolution of ligands by exponential enrichment (SELEX) process. In this study, the non-SELEX in silico strategy was used to simulate the process of aptamer synthesis and subsequent affinity evaluation. We hypothesized that a candidate RNA aptamer could function as an antagonist to nuclear thyroid hormone receptors (TRs), thereby inhibiting their interaction with thyroid hormone response elements (TREs). Using knowledge-based approaches, TRE sequences were retrieved from the literature, and representative loci across the human genome were modeled. Through RNA structure prediction, molecular docking, and molecular dynamics simulations, several single-stranded RNA aptamers with strong binding affinity toward TRs were identified. Among them, one candidate demonstrated the most favorable interaction with thyroid hormone receptor alpha. Pending experimental validation, this aptamer holds potential as a novel therapeutic agent for hyperthyroidism by acting as a TR-blocking molecule.

Tumor angiogenesis, largely driven by VEGFR2 signalling, is a critical hallmark of cancer progression. In this study, we employed a structure-based virtual screening approach of pyrrolopyrimidine analogs from a natural product database, combined with density functional theory (DFT), molecular docking, and molecular dynamics (1 μs) simulations, to identify potential VEGFR2 inhibitors. Binding free energy (MM-GBSA) calculations were used to refine candidate selection. Three top-ranking compounds, CNP0279613, CNP0102100, and CNP0004587, were identified, with CNP0279613 showing the most favourable stability and binding affinity. Biophysical validation using isothermal titration calorimetry confirmed strong binding of CNP0279613 to VEGFR2, while in vitro MTT assays in HUVEC cells demonstrated its superior anti-angiogenic activity compared to the other candidates. Notably, its inhibitory effect was comparable to that of Ramucirumab, an FDA-approved VEGFR2 inhibitor. Together, these computational and experimental findings highlight CNP0279613 as a promising lead scaffold for the development of next-generation anti-angiogenic therapies and warrant further optimization and in vivo evaluation.

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BRAF mutations were first discovered by Davies et al. in 2002. BRAF^V600E mutation is the most prevalent, accounting for approximately 90% of all BRAF mutations. BRAF^V600E mutations have been identified at varying frequencies across multiple human cancers, including malignant melanoma (70–90%), thyroid cancer (45–50%), colorectal cancer (5–20%), and others. In this study, we designed a series of pyrimidine-sulfonamide hybrids, inspired by first- and second-generation FDA-approved BRAF inhibitors such as sorafenib, dabrafenib, and vemurafenib. The designed compounds were intended to target the αC-OUT/DFG-IN conformation of the BRAF^V600E mutant protein. Eighteen compounds (B1–B18) were synthesized and characterized using spectral techniques. Molecular docking and MD simulations were carried out to assess their binding affinity and stability with the BRAF^V600E protein. Kinase inhibition was assessed using a BRAF^V600E specific assay, and anticancer activity was tested against HCT-116, A375, HT-29, and TPC-1 cell lines. Among the tested derivatives, B14 exhibited the highest cytotoxicity against HCT-116, B8 was most effective against A375, B18 showed potent inhibition in HT-29, and B3 demonstrated the strongest activity in TPC-1 cells. All four compounds exhibited activity comparable to sorafenib. Notably, B4 emerged as the most potent BRAF^V600E kinase inhibitor in assays.

A series of six new compounds (1–6) were synthesized through the implementation of chemical reactions, employing the starting material 4-aminobenzophenone and six distinct aldehyde derivatives. The antiproliferative activities of the compounds 1–6 were evaluated to assess their potential as anticancer agents. Considering that structurally similar compounds have been reported as tubulin polymerization inhibitors, in silico studies were conducted to investigate the binding interactions of the synthesized derivatives with the colchicine-binding site of tubulin. Molecular docking studies indicated favorable binding affinities for all compounds toward the target site. Furthermore, molecular dynamics (MD) simulations confirmed the stability of the ligand–tubulin complexes, supporting the potential of these 4-aminobenzophenone derivatives as candidate tubulin-targeting anticancer agents.

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This study investigates the potential of isolated compounds from Paeonia lactiflora to mitigate heat stress-induced male infertility in Drosophila melanogaster, with egg-hatching rates as a quantitative fertility indicator. Exposure to thermal stress (27.5 °C) significantly impaired male fertility, resulting in egg viability declining to 16.18–23.08%. Supplementation with 10 µM of paeoniflorin (1), benzoic acid (2), and albiflorin (4) significantly restored egg-hatching rates to 55.17–93.48%, demonstrating protective effects against heat stress-induced reproductive impairment. Immunofluorescence analysis of testis tissue revealed that these compounds maintained spermatogonia structural integrity under thermal stress conditions. Molecular docking analyses identified specific binding interactions between compounds 1, 2, and 4 with Vasa protein, characterized by distinct patterns of hydrogen bonding, van der Waals forces, and hydrophobic interactions. Paeoniflorin (1) exhibited the highest binding affinity (− 9.64 kcal/mol), followed by compound 4 (− 9.14 kcal/mol), while compound 2 demonstrated a lower binding affinity. Molecular dynamics simulations conducted over 200 ns confirmed the thermodynamic stability of these complexes, with root mean square deviation values converging around 0.2 nm for all compounds. Analyses of root mean square fluctuation, hydrogen bond numbers, and molecular contact surface area provided further evidence of complex stability. Moreover, the free energy landscape and MM/PBSA analyses revealed that van der Waals and electrostatic interactions make significant favorable contributions to the thermodynamics of the system. These findings elucidate the molecular mechanisms by which secondary metabolites from P. lactiflora protect against heat stress-induced male reproductive dysfunction, offering potential therapeutic strategies for mitigating heat-induced infertility.

Traditional usage and in vitro studies have previously proven the effects of soluble epoxide hydrolase (sEH) inhibitors isolated from Phyllostachys bambusoides. A phytochemical investigation of Phyllostachys bambusoides led to the isolation of six known compounds: one phenolic amide moschamine (1), three flavonoids, including tricin (2), salcolin A (3), and luteolin 6-C-α-L-arabinopyranoside (4), as well as two neolignans (5–6). The structures of these compounds were determined spectroscopically; their nuclear magnetic resonance spectra were compared to reported spectra. The sEH inhibitory activity of all isolated compounds was examined. Compounds 1‒4 exhibited strong sEH inhibitory activity with IC₅₀ values of 30.6, 57.5, 16.8, and 11.7 µM, respectively. Kinetic analyses of most potent compounds, 3 and 4, revealed that they were non-competitive inhibitors of sEH. The resulting molecular docking and molecular dynamics simulations have increased our understanding of the dynamic behavior of receptor–ligand binding between these compounds. Our findings suggest that flavonolignan and flavone derivatives from P. bambusoides leaves show promise as potential natural sEH inhibitors.