Journal of Computer-Aided Molecular Design最新文献

A series of ten chloro- and bromo-substituted isatin derivatives were synthesized and evaluated for their ability to inhibit the monoamine oxidase (MAO) enzymes. All compounds demonstrated more potent inhibition of MAO-A compared to MAO-B. The most potent MAO-A inhibitor was HIB2 (IC₅₀ = 0.037 μM), followed by HIB4 (IC₅₀ = 0.039 μM), while HIB10 (IC₅₀ = 0.125 μM) exhibited the most potent inhibition of MAO-B. HIB2 was identified as a specific MAO inhibitor with a selectivity index of 29 for MAO-A over MAO-B. The enzyme-inhibitor dissociation constants (K_i) for HIB2 and HIB10 were 0.031 μM and 0.036 μM, respectively, for MAO-A and MAO-B. Both HIB2 and HIB10 exhibited competitive and reversible inhibition. An analysis of the ADMET and PAMPA suggested that HIB2 is permeable to the blood–brain barrier (BBB). Molecular docking analysis revealed that HIB2 forms stable hydrogen bonds with Asn181 and Gln215 in the MAO-A ligand–protein complex. Dynamic analysis indicated the stability of HIB2 with MAO-A. These findings suggest that HIB2 is potent reversible MAO-A inhibitor, making this class of compounds potential therapeutic agents for neurological disorders.

Diabetes mellitus remains a major global health challenge, necessitating the search for potent and safer therapeutic agents. In this study, a series of novel pyrrolo-imidazolidinone derivatives (1–10) was designed and synthesized as potential anti-diabetic agents. Structural elucidation was carried out using HREI-MS, ¹H-NMR and ¹³C-NMR spectroscopy. The anti-diabetic potential of the compounds was evaluated in vitro against α-amylase and α-glucosidase enzymes. Among the synthesized derivatives, compounds 4, 5, and 7 exhibited the most potent inhibitory activity, with IC₅₀ valuesranging between 4.10 ± 0.30 to 2.10 ± 0.10 µM (α-amylase) and 4.80 ± 0.40 to 2.60 ± 0.20 µM (α-glucosidase), surpassing the reference drug acarbose (IC₅₀ = 4.20 ± 0.60 µM and 5.10 ± 0.10 µM, respectively). In silico studies, including molecular docking, pharmacophore modeling, and ADMET profiling, supported the experimental findings and provided insights into the structural features governing enzyme inhibition and drug-likeness. The results highlight pyrrolo-imidazolidinone derivatives as promising scaffolds for further development of effective anti-glycemic agents.

Multidrug-resistant (MDR) Klebsiella pneumoniae poses a significant global health concern, particularly in hospital setting where it causes severe and hard-to-treat infections. In this study, 329 fungal-derived compounds were screened for their potential to inhibit MrkD1P, a key fimbrial adhesin protein (PDB ID: 3U4K) involved in host tissue adhesion. Molecular docking analysis identified ochratoxin A (− 9.1 kcal/mol), bromadiolone (− 8.6 kcal/mol), and permethrin (− 8.2 kcal/mol) as top-performing candidates, exhibiting strong binding affinities and stable molecular interactions, including hydrogen bonding and hydrophobic contacts. These findings were reinforced by 100-nanosecond molecular dynamics (MD) simulations, which showed sustained ligand–protein stability, particularly for ochratoxin A. Free energy estimations using the MM/PBSA method further suggested the thermodynamic favourability of these interactions. Pharmacokinetic profiling (ADMET) indicated favourable absorption and distribution properties for all three compounds, with low toxicity predictions, though some hepatotoxicity was noted. Principal component analysis (PCA) demonstrated that ochratoxin A and permethrin induced substantial alterations in protein dynamics, suggesting ligand-specific structural effects. Experimental validation confirmed the antibacterial activity of ochratoxin A against K. pneumoniae, producing a 34 ± 0.67 mm inhibition zone at 100 µg/disc, surpassing ciprofloxacin (33 mm) with a MIC of 18.33 ± 0.72 µg/mL and MBC of 39.33 ± 1.36 µg/mL (p < 0.05). Collectively, these in silico and in vitro results highlight fungal metabolites, particularly ochratoxin A, as promising therapeutic leads against MDR K. pneumoniae. However, further in vivo investigations are required to establish their safety and clinical potential.

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Diabetes mellitus is a prevalent metabolic disorder characterized by impaired glucose metabolism. This study investigates the anti-diabetic potential of Cotula cinerea essential oil (EO) through in vivo, in vitro, and computational methodologies. In vitro enzyme inhibition assays demonstrated that C. cinerea EO effectively inhibits α-amylase and α-glucosidase, indicating its potential role in glucose regulation. In vivo studies further confirmed its hypoglycemic effects. GC–MS analysis identified 31 bioactive compounds within the EO. Molecular docking studies revealed that six of these compounds exhibited strong binding affinities to α-amylase and α-glucosidase, comparable to those of the standard drug acarbose (ARE). Among them, cis-verbenyl acetate (CVA) and β-terpineol (βTP) showed the highest docking scores against both enzymes. ADMET analysis confirmed their favorable pharmacokinetic properties, drug-likeness, and low toxicity risks. Molecular dynamics simulations demonstrated the stable binding of CVA and βTP with both enzymes, exhibiting lower RMSD and RMSF values compared to ARE, along with favorable Rg and SASA parameters. MM-PBSA calculations further validated their strong binding affinities. Density Functional Theory calculations provided deeper insights into the electronic characteristics of CVA and βTP, revealing their frontier molecular orbitals distributions and energy gap (∆E) values. The molecular electrostatic potential analysis identified key electron-rich and electron-deficient regions, suggesting potential interaction sites with the target enzymes. The observed reduction in ∆E values under aqueous conditions indicated increased molecular stability and reactivity within physiological environments, further supporting their role in enzyme inhibition. Overall, this study highlights C. cinerea EO as a promising natural source for diabetes management. The integration of in vivo, in vitro, and computational approaches offers compelling evidence for its therapeutic potential. Nevertheless, further experimental validation is necessary to assess its clinical applicability.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become the third case of widespread coronavirus infection. Together with the other two viruses, the SARS-CoV-2 virus is highly pathogenic, and some strains have a mortality rate of more than 1%. Moreover, it has become clear that coronaviruses mutate quite often, which reduces the effectiveness of available vaccines and forces the regular creation of new ones. The main viral protease M^pro is a suitable target for direct-acting drugs. Currently, there is only one recommended anticoronavirus drug, nirmatrelvir, which, however, does not have all the properties necessary for widespread and effective use. Thus, the development of a highly selective and effective protease inhibitor that can be taken orally still remains relevant. In this work, we performed an in-depth literature review of M^pro inhibitor studies and conducted extensive molecular dynamics simulations of M^pro-inhibitor complexes with computational prediction of binding ability and ADME (absorption, distribution, metabolism and excretion) properties of new compounds. On the basis of the literature review we composed a set of criteria that a potent inhibitor must meet. Then we created a set of possible inhibitors and their parts, which presumably allows all the necessary properties, namely, high affinity for the viral enzyme, selectivity, bioavailability and solubility, to be achieved.

This study addresses the urgent need for an AI model to predict Anaplastic Lymphoma Kinase (ALK) inhibitors for Non-Small Cell Lung Cancer treatment, targeting the ALK-positive mutation. With only five Food and Drug Administration approved ALK inhibitors currently available, effective drugs remain in demand. Leveraging machine learning (ML) and deep learning (DL), our research accelerates the precise screening of novel ALK inhibitors using both ligand-based and structure-based approaches. In ligand-based approach, an ensemble voting model comprising three base learners to classify potential ALK inhibitors, achieving promising retrospective validation results. Notably, the ML-based XGBoost algorithm exhibited compelling results with external validation (EV)-f1 score of 0.921, EV-Average Precision (AP) of 0.961, cross-validation (CV)-f1 score of (0.888pm 0.039) and CV-AP of (0.939pm 0.032). Besides, the DL-based Artificial Neural Network (ANN) model demonstrated comparative performance with EV-f1 score of 0.930, EV-AP of 0.955, CV-f1 score of (0.891pm 0.037) and CV-AP of (0.934pm 0.040). For structure-based approach, an XGBoost consensus docking model utilized scores from three molecular docking programs (GNINA 1.0, Vina-GPU 2.0, and AutoDock-GPU) as features. Combining these two approaches, we virtually screened 120,571 compounds, identifying three promising ALK inhibitors, CHEMBL1689515, CHEMBL2380351, and CHEMBL102714, that bind to the protein’s pocket and establish hydrophobic contacts in the hinge region through their ketone groups, resembling Alectinib’s interaction. Comparative analysis revealed traditional ML models outperformed Graph Neural Networks (GNN), highlighting the critical role of feature engineering and dataset size importance. The study recommends further in vitro testing to validate the prospective screening performance of these models. A graphical user interface is available at https://huggingface.co/spaces/thechuongtrinh/ALK_inhibitors_classification.

Due to the complexity of molecules, molecular learning requires a large amount of molecular data. However, labeled data is typically limited, making self-supervised pretraining methods essential. Despite this, current pretraining methods often fail to sufficiently focus on both local and global molecular information. In this study, we propose a multi-modality self-supervised learning framework that simultaneously captures local and global information. Specifically, we encode SMILES sequences and molecular graphs separately and use a unified fusion approach to strengthen the interaction between the two modalities. Moreover, in the molecular graph encoding, we independently capture global and local information, and enhance the attention to bond features through information fusion. Additionally, we introduce the FA-FFN module to aggregate periodic features of the molecule. Experimental results show that MoleTGL exhibits superior performance compared to existing methods on seven classification tasks and six regression tasks related to molecular property prediction, and ablation studies confirm the effectiveness of local and global feature fusion and the superiority of the methods for acquiring local and global information.

With the rapid advancement of biotechnology, protein generation and design based on generative models have demonstrated extensive applications in drug development, vaccine research, and biocatalysis. This research proposes a protein generation method based on the generalized diffusion model, which breaks through the traditional diffusion model’s reliance on Gaussian noise, enables more flexible protein sequence generation, and preliminarily verifies its advantages. Specifically, protein sequences were first encoded using one-hot encoding and input into the diffusion model to generate novel sequences. Subsequently, the tertiary structures of the generated proteins were predicted using AlphaFold, followed by structural alignment and backbone distance calculation via PyMOL to select the optimal sequences. The predicted derivative protein sequence A_005 was screened from the generated sequences and subjected to an affinity assay with Protein A parental. Experimental results revealed that A_005 exhibited remarkably high affinity, as well as a satisfactory dissociation rate and association rate. The findings demonstrate that the protein generation method based on the generalized diffusion model can effectively design protein sequences with high structural and functional similarity to target sequences. While prior studies have shown that both DDPM and generalized diffusion models achieve high generation quality, the generalized diffusion model outperforms in terms of task adaptability. Our research not only opens new technological pathways for protein design but also lays a solid foundation for future applications in biomedicine, providing significant theoretical and experimental evidence for subsequent drug development.

TGF-β receptor I kinase plays a significant role in cancer biology and is a well-established target for cancer drug development, as evidenced by active molecules like Galunisertib (LY2157229). Computational studies were conducted to analyse the catalytic site of TGF-β receptor I kinase, identifying key amino acid residues essential for binding. Based on these findings, Alicyclic fused pyrazole derivatives were designed. The target molecules were synthesized through a multi-step process, with an important intermediate obtained via Suzuki coupling, followed by various ligand and catalyst optimizations. A total of thirteen molecules were synthesized by optimizing temperature, solvent, and base. After characterization, the synthesized, Alicyclic fused pyrazole derivatives were screened for TGF-β receptor I kinase inhibition and in vitro cytotoxic activity. To further elucidate their binding mechanism, molecular docking and molecular dynamics studies were performed. The most active compound 16c, was subjected to in silico ADME screening, which revealed a favorable pharmacokinetic profile. Molecular Dynamics simulation study indicated that specific aminoacid residue interaction with TGF-β receptor I kinase. Additionally, DFT calculations were conducted on the active molecules to gain deeper insights into their electronic properties, supporting their potential as effective anticancer agents.

Fluorine-18-labeled radiopharmaceuticals are central to PET-based oncology imaging, yet comparative evaluations of their mechanistic behavior and diagnostic potential remain fragmented. In this study, we present a multidimensional in silico framework integrating pharmacokinetic modeling, structural ADMET prediction, and unsupervised clustering to systematically evaluate five widely used ¹⁸F-labeled PET radiopharmaceuticals: [¹⁸F]FDG, [¹⁸F]FET, [¹⁸F]DOPA, [¹⁸F]FMISO, and [¹⁸F]FLT. Each radiopharmaceutical was simulated using a harmonized three-compartment model in COPASI to capture uptake dynamics under both normal and pathological conditions. Key pharmacokinetic parameters, including area under the curve, tumor-to-normal tissue ratios, and early-phase uptake slope, were computed and subjected to local sensitivity analysis to assess model robustness. In parallel, in silico ADMET descriptors were extracted via ADMETlab 3.0, providing quantitative insight into lipophilicity, permeability, distribution volume, and metabolic clearance. All features were normalized and integrated into a joint dataset for principal component analysis and hierarchical clustering. The resulting stratification revealed two distinct mechanistic clusters: [¹⁸F]FDG and [¹⁸F]FLT were characterized by irreversible trapping and high intracellular retention, whereas [¹⁸F]FET, [¹⁸F]DOPA, and [¹⁸F]FMISO exhibited transporter-mediated uptake with greater sensitivity to permeability and efflux parameters. Diagnostic strengths varied by context, with [¹⁸F]FET optimal for early-phase imaging and [¹⁸F]FMISO demonstrating superior tumor selectivity at later timepoints. ADMET features reinforced kinetic signatures, supporting the structure–function rationale underlying radiopharmaceutical performance. This multidimensional in silico evaluation establishes a mechanistically interpretable platform for PET radiopharmaceutical profiling and stratification, advancing preclinical radiopharmaceutical selection and informing precision multiradiopharmaceutical imaging protocols in oncology. However, while our computational approach offers a mechanism-driven platform for radiopharmaceutical stratification, future validation against experimental PET imaging data in both healthy individuals and patients with relevant pathologies is essential to confirm its predictive value and clinical applicability.