Molecular Diversity最新文献

Protein arginine methyltransferase 5 (PRMT5) is a key epigenetic enzyme that catalyses symmetric arginine methylation on histone and non-histone proteins, influencing chromatin organisation, RNA splicing, and oncogenic signalling. Its overexpression and dependency in MTAP-deleted cancers such as glioblastoma, pancreatic adenocarcinoma, and non-small cell lung carcinoma highlight its therapeutic relevance. This study presents an integrative computational framework combining quantitative structure-activity relationship (QSAR) modelling, molecular docking, molecular dynamics (MD) simulations, and network pharmacology to identify potential PRMT5 inhibitors. The best QSAR models based on machine learning techniques used different fingerprint representations and algorithms to describe chemical structures; Random Forest models trained on PubChem and MACCS descriptor combinations provided the most accurate predictions. Analysis of consensus QSAR models identified two highly active PRMT5 inhibitor candidates (CHEMBL4539612 and CHEMBL4577464), with high affinity for binding (- 13.5 to - 13.7 kcal/mol) to the PRMT5 active site and interactions similar to those of the known clinical PRMT5 inhibitor ONAMETOSTAT. Molecular dynamics simulations showed that both candidate molecules-maintained stability throughout the PRMT5 catalytic cleft, due to consistent hydrogen bonding, compact conformations, and low negative binding free energy values determined by MM-GBSA calculations. Network pharmacology analysis indicated that PRMT5 and its interacting partners are mainly associated with histone arginine methylation and spliceosomal assembly, processes that are frequently dysregulated in MTAP-deficient cancers. These findings suggest CHEMBL4539612 and CHEMBL4577464 as promising scaffolds for the development of selective PRMT5 inhibitors in epigenetic cancer therapy.

Drug-drug interactions (DDIs) are a significant issue in drug discovery, impacting research efficiency and patient safety. Precise prediction of DDIs is important, particularly when drugs are co-administered. The combination of heterogeneous data sources that reflect drug relationships and properties can greatly enhance predictive accuracy. This paper proposes a new Capsule-enclosed Coordinate Attention-based Dual Batch Depthwise Convolutional Knowledge Distillation (CC-DBDKD) model for DDI prediction. The input data drawn from the DrugBank dataset is preprocessed with the RDKit to standardize SMILES strings into their canonical representations. Various techniques of molecular fingerprint generation, such as Extended Connectivity Fingerprints, MACCS keys, PubChem Fingerprints, 3D molecular fingerprints, and molecular dynamics fingerprints, are used to map drug chemical structures onto feature vectors. Drug similarities are subsequently calculated by the Tanimoto coefficient, and the Structural Similarity Profile (SSP) is calculated as an average of these fingerprint types. A lightweight model, CC-DBDKD, improves DDI prediction by introducing capsule networks to learn spatial hierarchies and complex drug relationships. Coordinate attention mechanisms improve feature extraction by attending to key interaction patterns. Adding dual-batch depthwise convolutional layers improves computational efficiency to support scalability with large datasets. In addition, knowledge distillation reinforces the model by mapping knowledge from a teacher model to a student model, enhancing accuracy and robustness. The proposed model realizes superior accuracy values of 0.987 and 0.989 and an F1-score of 0.986, which outshines other prevailing models like CNN, CNN-LSTM, Autoencoder, and D-CNN. The outcomes position the CC-DBDKD model as a strong and scalable instrument for accurate DDI prediction.

Parkinson's disease (PD) is a neurodegenerative disorder that causes irreversible damage to brain structures through neurotransmitter oxidation, leading to motor symptoms like tremors and muscle rigidity. Although existing therapies target monoamine oxidase B, recent research has highlighted​​ a correlation between adenosine A₁ and A_2AR receptors in inhibiting dopamine reuptake, as observed in rats. Chlocarbazomycins (CCB), carbazole derivatives with neuroprotective properties, show potential for central nervous system (CNS) therapies. This study examines the structural and bioactivity properties of four carbazomicin derivatives (CCB1-4) using quantum-level Density Functional Theory (DFT) calculations, virtual screening, and a predictive pharmacokinetics study. The results showed that different environments (water, DMSO, and chloroform) had minimal impact on the reactivity of CCB1-4 derivatives. Structure-based virtual screening revealed that the heteroaromatic nature of CCB1-4 closely resembles that of adenosine (ADN), the endogenous ligand for A₁R receptors. Molecular docking showed that CCB3 had the highest affinity for the receptor, with a binding energy of - 8.6 kcal/mol at the ADN agonist site. Molecular dynamics simulations confirmed the stable binding of CCB3, with a free energy of - 25.9 kcal/mol, suggesting that CCB3 may act as an antagonist to ADN in A1R modulation. The results of predictive pharmacokinetic studies indicate that the compound exhibits high passive cell permeability (P_{app, A→B} > 10 × 10^- 6 cm/s) and low hepatic clearance, which collectively ensure the safe activity of the compound in the CNS. These findings suggest that CCB3 has potential in PD treatment.

An efficient one-pot synthetic strategy has been developed for the construction of substituted 7-chloroindolizine derivatives using 4-chloropyridine hydrochloride (1) and substituted phenacyl bromides 2(a-i) as key starting materials. The reaction proceeds via a cascade cyclization under mild and operationally simple conditions, affording a structurally diverse library of indolizine (4a-l) frameworks excellent to good yields. The synthesized compounds were rigorously characterized by ¹H and ¹³C NMR spectroscopy and high-resolution mass spectrometry (HRMS). Biological evaluation identified two active derivatives, 4g and 4h, with antibacterial activity against Staphylococcus aureus and Escherichia coli (30% and 90% inhibition, respectively). Both exhibited antioxidant potential, with 4h showing the highest ROS scavenging (61% at 100 µg). In vitro assays further revealed selective COX-2 inhibition by 4h (IC₅₀ = 10.24 µM; SI = 3.09), comparable to celecoxib. The DFT analysis revealed a moderate HOMO-LUMO gap (4.07 eV) with well-defined donor and acceptor regions supporting efficient charge transfer. Global reactivity descriptors classify the molecule as a strong electrophile with notable electron-donating capability, while MEP mapping highlights carbonyl and hydroxyl oxygens as key reactive sites. These electronic features collectively indicate strong potential for biomolecular interaction. Computational studies supported these findings, with docking, MM/GBSA, and 200 ns MD simulations confirming stable, energetically favourable interactions of 4h with COX-2 and S. aureus DHFR. Collectively, these results highlight 4h as a promising scaffold for developing multifunctional anti-infective and anti-inflammatory agents. This work underscores the value of a streamlined synthetic approach for rapidly generating heteroaryl scaffolds with significant therapeutic relevance in antimicrobial, antioxidant and anti-inflammatory drug discovery.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which cholinergic dysfunction plays a central role. Inhibition of acetylcholinesterase and butyrylcholinesterase remains a validated therapeutic approach for managing AD symptoms. Over the past decade (2015-2025), 1,3,4-thiadiazole derivatives have gained considerable attention as promising scaffolds for cholinesterase inhibition owing to their favorable electronic configuration, hydrogen-bonding potential, and metabolic stability. This review comprehensively analyzes recent progress in the synthesis and biological evaluation of 1,3,4-thiadiazole-based cholinesterase inhibitors, with an emphasis on structure-activity relationship trends supported by molecular docking insights. Substitution with electron-withdrawing or heteroaryl groups has been found to enhance the binding affinity toward AChE and BuChE, while some derivatives also exhibit activity against carbonic anhydrase, α-glucosidase, α-amylase, and antioxidant systems, reflecting scaffold versatility. This review further highlights the docking interactions with catalytic residues that validate the observed experimental potency. Finally, key limitations and future directions are discussed, emphasizing rational structure modification, computationally guided design, and green synthetic approaches to develop brain-penetrant and pharmacologically optimized 1,3,4-thiadiazole-based anti-Alzheimer's agents.

We propose FRAIL (Fragment-based Reinforcement Learning for Inhibitors), a generative AI framework that integrates fragment-based molecular design, multi- objective reinforcement learning, and molecular modeling to accelerate inhibitor discovery. Several deep generative models were fine-tuned on FAAH-1 (Fatty Acid Amide Hydrolase 1)-specific dataset and systematically benchmarked, with the best-performing model incorporated into FRAIL. The framework employs a customized reward function that jointly optimizes physicochemical properties and predicted bioactivity (pIC₅₀) to guide molecular generation toward FAAH- favorable chemotypes. FRAIL generated structurally novel, fragment-grown compounds exhibiting high predicted binding affinity, desirable drug-likeness, and synthetic accessibility. These findings demonstrate FRAIL's capability to enhance rational drug design and provide a reproducible pipeline for the discovery of experimentally viable FAAH inhibitors. Our pipeline source code is released in https://github.com/AppliedAI-Lab/FRAIL .

Computational techniques have become powerful tools for studying biological systems, including receptor-ligand (R-L) complexes. In medicinal chemistry, these in silico approaches are widely used for modeling and predicting molecular interactions, as well as for designing new ligands with biological activity. However, obtaining a direct correlation between the structure and activity of a set of active compounds is a challenging task. This study aims to develop a computational pipeline to find a direct correlation between structure and acetylcholinesterase (AChE) inhibitory activity across a structurally diverse set of 224 Amaryllidaceae alkaloids and synthetic derivatives. Standard docking protocols failed to generate reliable correlations with experimental data, and although the inclusion of molecular dynamics (MD) simulations improved performance, the results remained insufficient for robust prediction. Incorporation of quantum theory of atoms in molecules (QTAIM) analyses on MD-refined geometries was essential to capture key R-L interactions, yielding a strong correlation with relative IC₅₀ values (R = - 0.9131). This approach not only explained differences in activity among structurally related compounds but also distinguished active, moderately active, and inactive ligands across multiple alkaloid families. For the first time, a QTAIM analysis is reported providing detailed insights into the molecular interactions stabilizing AChE-ligand complexes, including natural alkaloids, as well as synthetic dual-site inhibitors designed to engage both the catalytic active site and the peripheral anionic site of the enzyme. These findings suggest that simple appropriately combined computational methodologies can yield predictive and explanatory models applicable to chemically diverse scaffolds, supporting the rational design of novel AChE inhibitors.

Dengue infection remains a major global public health challenge, with no specific antiviral therapy currently available. The dengue virus non-structural protein 1 (NS1) exists in both intracellular and secreted forms playing a pivotal role in viral replication, immune evasion, and pathogenesis, particularly by contributing to endothelial disruption and vascular leakage during severe disease, thereby making it a promising therapeutic target. In silico screening identified berberine, betulinic acid, and ursolic acid as top candidates, exhibiting high binding affinities and stable interactions within the NS1 binding pocket. These computational predictions were further validated by biophysical assays, which demonstrated strong and specific binding interactions between the purified NS1 protein and the selected compounds. All three compounds significantly reduced viral genome levels, with the highest inhibition observed for berberine (60%), and followed by betulinic acid (40%) and ursolic acid (28%). Consistently, berberine showed the most potent inhibition of both intracellular and extracellular NS1. Overall, these findings highlight the inhibitory potential of natural compounds against DENV NS1 and provide a strong foundation for the development of NS1-targeted antivirals as a novel therapeutic strategy against dengue infection.