Journal of Computer-Aided Molecular Design最新文献

Boron nitride nanotubes (BNNTs) have garnered significant interest due to their exceptional mechanical strength, chemical stability, and biocompatibility. However, their limited solubility in aqueous environments poses a major challenge for biomedical applications. In this study, we employ density functional theory (DFT) calculations to explore the impact of hydroxyl (–OH) and amine (–NH₂) functionalization on the structural, electronic, and solubility characteristics of BNNTs. The pristine (5,5) BNNT exhibits a bandgap of 4.46 eV, which decreases upon functionalization, indicating enhanced electronic tunability. Structural modifications, including bond length elongation and charge redistribution, further influence the nanotube’s chemical reactivity and interaction with surrounding molecules. A crucial aspect of this work is the investigation of carrier solubility, which reveals a strong correlation between hydration and system stability. The Gibbs free energy of solvation becomes increasingly negative, from − 602.02 kJ/mol for BNNT4Am to − 720.18 kJ/mol for BNNT4Am-6W, suggesting enhanced solubility in aqueous environments. Stronger interactions between the functionalized BNNTs and water molecules suggest them as promising candidates for drug delivery applications. Additionally, drug interaction studies were carried out between BNNT4Am and Indole-3-Carbinol, which reflects weak electrostatic interactions and polarization effects contributing to the favorable energetics and stability of nanobiohybrid complex formation.

The highly aggressive primary brain tumor, glioma, presents significant therapeutic challenges, particularly in its diffuse form, which remains resistant to curative treatment even after surgical intervention. Conventional approaches such as surgery, radiotherapy, and chemotherapy often fail to achieve satisfactory outcomes, underscoring the urgent need for more effective targeted therapies. In this study, we have developed novel AKT inhibitors—compounds 3260-0411, V012-5231, and V016-4965. These compounds demonstrate a substantial reduction in both AKT protein and mRNA levels in U251 and T98G glioma cells. Furthermore, our in vitro experiments reveal that these inhibitors effectively suppress AKT1 enzyme activity and induce apoptosis in glioma cells. Molecular dynamics simulations indicate that all three compounds exhibit excellent dynamic stability when bound to AKT; notably V016-4965 demonstrates the highest binding stability among them. Collectively, our findings suggest that compounds 3260-0411, V012-5231, and V016-4965 hold great promise as targeted therapies against AKT for treating glioma—a challenging malignancy with limited management options.

High failure rates in drug development are predominantly driven by suboptimal ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, with human oral bioavailability (HOB) serving as a critical determinant of therapeutic efficacy and safety. Traditional HOB assessment methods, reliant on animal models and clinical trials, face inherent limitations in cost, scalability, and reproducibility. To address these challenges, this study proposes a deep learning framework integrating the directed message-passing neural network (D-MPNN) from the Chemprop tool with RDKit-derived molecular descriptors, enhancing predictive accuracy through hybrid representations of atomic/bond-level graph features and global physicochemical properties. Bayesian optimization automated hyperparameter tuning, while ensemble learning (20 models) ensured robustness for model development. The optimized model achieved an AUC of 0.8299 and accuracy of 77.65% on internal validation, outperforming existing tools with 75% accuracy on external FDA-approved drugs. Interpretability analysis identified critical substructures correlated with high HOB, providing actionable insights for rational drug design. This work establishes a novel method for high-throughput screening of candidates with favorable bioavailability, highlighting the potential of deep learning to decode complex structure-property relationships in pharmaceutical optimization.

This research aims to develop a predictive model to support the design of new 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) inhibitors based on the pseudothiohydantoin scaffold, offering potential for novel treatments of metabolic disorders. The Quantitative Structure–Activity Relationship (QSAR) analysis was performed on 56 2-aminothiazol-4(5h)-one derivatives, for which the 11β-HSD1 inhibitory activity was previously reported. Gaussian software was employed for geometry optimization, while Dragon software was used to calculate the molecular descriptors. The study used an Artificial Neural Network (ANN) algorithm for regression analysis between the top ten preselected descriptors and the activity of the studied analogs. A predictive model was developed using a network architecture 10-11-1 with a Broyden–Fletcher–Goldfarb–Shanno learning algorithm. The model’s reliability was supported through cross-validation and y-randomization strategies. The model exhibited high accuracy with a determination coefficient (R²) of 0.9482, and its validity was confirmed through internal validation with a cross-validated R² (Q²) of 0.9944. Three classes of 3D descriptors (GETAWAY, 3D-MoRSE, and RDF descriptors) and four groups of topological indices (GALVEZ, 2D autocorrelations, 2D matrix-based descriptors, and Burden eigenvalues) were used to generate the QSAR model. The developed model was applied to predict the 11β-HSD1 inhibitory activity of four designed series of 2-aminothiazol-4(5h)-one derivatives, suggesting that compounds with cyclohexyl and 2-(tetrahydro-2H-pyran-2-yl)methyl residues substituted at the amino group, and various substituents at C-5 of the thiazole ring, could be potential candidates for upcoming chemical synthesis and biological assessment.

This study combines experimental and computational approaches to investigate the molecular geometry and physicochemical properties of vildagliptin (VILD). Using methods such as UV-Vis, spectrofluorimetry, FTIR/Raman, and circular dichroism alongside DFT, molecular docking, and dynamics simulations, a reliable molecular model was obtained that aligns closely with X-ray crystallographic data. This model enabled accurate predictions of vibrational frequencies and systematic assignments of vibrational modes. Analyses, including Hirshfeld surface mapping, molecular electrostatic potential, HOMO-LUMO energetics, Fukui indices, and natural population analysis, provided clear insights into VILD’s reactivity, while NBO and TD-DFT studies elucidated key stabilizing interactions and high-energy electronic transitions. NTO visualization further clarified orbital dynamics, and circular dichroism measurements explained the molecular basis of the Cotton effect. Additionally, molecular docking and molecular dynamics simulations confirmed the formation of stable complexes with EGFR, VEGFR2, and HER2 receptor proteins, suggesting potential anticancer activity. The main purpose of this publication is to fill existing gaps in our understanding of VILD’s molecular behavior and offer a robust foundation for rational drug design and improved therapeutic strategies.

To comprehend the olfaction process at a microscopic level, further elucidation of its multiple stages was essential. The content of this study served as an initial step towards achieving this goal via the examination of the adsorption of geosmin molecules on polar bear and camel olfactory receptors. The tested odorant, which is a common volatile sesquiterpenoid produced by microorganisms, was responsible for degrading the quality of various beverages, foods, and drinking water due to its unpleasant “earthy/musty” off-flavor. Indeed, the statistical physics modeling using the single layer model with two types of cavities was crucial to theoretically analyze the responsiveness of polar bear Ursus maritimus umOR11A1 and camel Camelus ferus cfOR11A1 to geosmin molecules. Indeed, fitting findings indicated that the values of the numbers of geosmin per binding cavity type 1 or 2 were lower than 1 for umOR11A1 (mixed orientations) and higher than 1 for cfOR11A1 (non-parallel orientations). The values of the molar adsorption energies relative to the two types of cavities, ranging from 10.97 to 21.69 kJ/mol, showed that process of adsorption was physical and exothermic. In addition, statistical physics modeling was also applied to theoretically characterize the two mammalian olfactory systems and quantitatively investigate the olfactory sensitivity. Additionally, a density functional theory (DFT) calculation incorporating the quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) have been conducted to enhance our understanding of the nature and types of forces contributing to the stability of the geosmin ligand. A docking study has been employed to elucidate the interaction mechanism between geosmin and both target receptors, Ursus maritimus umOR11A1 and Camelus ferus cfOR11A1, including a comparative description.

Eg5 is a mitotic kinesin motor protein essential for the formation of bipolar spindles during cell division. Its inhibition disrupts mitosis, leading to cell cycle arrest and apoptosis in cancer cells. This makes Eg5 a promising target for chemotherapeutic interventions, especially in cases resistant to traditional treatments. In this study, a drug repurposing strategy was employed to design and synthesise quinoline-based Schiff base derivatives as potential Eg5 inhibitors. These compounds were subjected to in vitro biological evaluations, including cytotoxicity testing against the human breast cancer cell line MDA-MB-231 and the normal mouse fibroblast cell line L929 using the MTT assay. Enzymatic assays targeting Eg5 were also conducted. Among the synthesised molecules, compound (5) demonstrated significant Eg5 inhibition in enzymatic assays, with an IC₅₀ of 2.544 ± 0.810 µM in the Malachite Green assay and 4.03 ± 2.027 µM in the steady-state ATPase assay, and moderate inhibition against triple-negative breast cancer cells (MDA-MB-231). Computational studies, including molecular docking, molecular dynamics simulations, and MM/GBSA free energy calculations, were performed to analyse binding interactions. ADMET properties were predicted using the QikProp module. The findings suggest that targeting mitosis through Eg5 inhibition may offer a strategic approach in chemotherapy, potentially enhancing treatment efficacy.

Cyclic peptides, prized for their remarkable bioactivity and stability, hold great promise across various fields. Yet, designing membrane-penetrating bioactive cyclic peptides via traditional methods is complex and resource-intensive. To address this, we introduce CCPep, an AI-driven de novo design framework that combines reinforcement and contrastive learning for efficient, customizable membrane-penetrating cyclic peptide design. It assesses peptide membrane penetration with scoring models and optimizes transmembrane ability through reinforcement learning. Customization of peptides with specific properties is achieved via custom functions, while contrastive learning incorporates molecular dynamics simulation time series to capture dynamic penetration features, enhancing model performance. Result shows that CCPep generated cyclic peptide sequences have a promising membrane penetration rate, with customizable chain length, natural amino acid ratio, and target segments. This framework offers an efficient tool for cyclic peptide drug design and paves the way for AI-driven multi-objective molecule design.

This study evaluated the antidiabetic efficacy of novel benzothiophenes using in silico, in vitro and in vivo methods. The synthesis of benzo[b]thiophene-2-carbohydrazide, specifically the Schiff base of benzo[b]thiophene (2, 3, 6) and the 1,3,4-oxadiazole adducts (4, 5, and 7) was performed through a cyclization reaction of the corresponding intermediates, compounds 2, 3 and 6. The cyclization was carried out by reacting the hydrazones (2, 3 and 6) with copper triflate (Cu(OTf)₂) as the catalyst and potassium carbonate (K₂CO₃) as a base in polar solvents such as N, N-dimethylformamide (DMF). The identity of these compounds was confirmed through comprehensive spectroscopic characterization, including infrared (IR) spectroscopy, carbon-13 nuclear magnetic resonance (¹³C NMR), proton nuclear magnetic resonance (¹H NMR), and high-resolution mass spectrometry (HRMS). Molecular docking, molecular dynamics simulation (200 ns), density functional theory (B3LYP, 6-31G), ADMET, and in vitro α-amylase inhibitory studies of the synthesized benzothiophenes were conducted. Also, the antihyperglycaemic activity of the top-ranked benzothiophenes was evaluated in glucose-loaded mice. Extensive structural characterization of the synthesized Schiff bases and oxadiazole adducts was performed. The molecular docking studies identified the synthesized compounds as potential α-amylase inhibitors, with binding affinities of -9.0, -8.5, and − 8.1 kcal/mol, respectively. Quantum chemical and ADMET studies further indicated the compounds as promising drug candidates. The in vitro inhibitory studies showed that 4 demonstrated the lowest IC₅₀ value of 0.032 µM compared to 2 (0.035 µM) and acarbose (0.09 µM). Comprehensive toxicity and histological studies of the compounds are recommended for further studies. (2 and 4) elicited good α-amylase inhibitory potential with IC₅₀ values of 0.035 and 0.032 µM.

Bovine viral diarrhea virus (BVDV) p7 functions as a viroporin for the ion balance and membrane permeabilization. Blocking the function of the viroporin is a promising strategy for the treatment of viral infection. Previous studies have demonstrated that the antiviral drug amantadine inhibits BVDV replication by inhibiting BVDV p7 activity. However, the mechanism by which amantadine acts against BVDV p7 remains unclear. In this study, AlphaFold2, molecular docking and molecular dynamics (MD) simulations were employed to investigate the binding sites of amantadine on BVDV p7. Structural analysis by AlphaFold2 and MD simulations showed that BVDV p7 may undergo antiparallel oligomerization, forming a stable hexamer that generates a pore channel. Notably, residues E21, Y25, L28, and R34 within the channel are likely involved in ion transport. Subsequently, the interaction of amantadine with BVDV p7 hexamer was investigated by docking studies and MD simulations analysis, indicating residues Y25 and L28 by van der Waals forces, alkyl and Pi-Alkyl interactions with amantadine. Importantly, the hydrogen bonding was observed between the -NH₃⁺ group of amantadine and residue Y25. By integrating these findings with the potential hexameric assembly of BVDV p7, we further proposed a potential ion channel model in which E21, Y25 and R34 are hypothesized to selectively recruit and dehydrate ions, while residue L28 acts as a hydrophobic restrictor, limiting the free movement of water. The binding of amantadine to residues Y25 and L28 likely disrupts ion transport. Our findings provide possible structural insights into the BVDV p7 ion channel and offer a mechanistic explanation for the inhibitory of amantadine on BVDV p7-mediated ion channel conductance, though experimental validation remains necessary.