Molecular Diversity最新文献

Gastric cancer (GC) risk is shaped by environmental exposures such as benzo[a]pyrene (BaP). Here, we systematically identified BaP-toxicological targets and dissected their contribution to GC development. BaP-related targets were independently predicted with stringent filters from ChEMBL, Similarity Ensemble Approach (SEA) and PharmMapper databases, while GC-related targets were mined from the Comparative Toxicogenomics Database (CTD), GeneCards and OMIM databases. Overlapping targets were subjected to protein-protein interaction (PPI) network construction, functional enrichment analysis and molecular docking. We then integrated multi-omics data using ten clustering algorithms to identify the consensus GC subtypes, which were subsequently employed 101 machine learning combinations to develop a consensus benzo[a]pyrene-related signature (CBRS) for GC patients. As a result, we identified seven hub toxicological targets: ALB, HSP90AA1, ESR1, INS, TP53, TNF, and EGFR, underscoring their potential central roles in BaP-driven GC pathogenesis. These targets are enriched in the MAPK, Lipid and atherosclerosis, and PI3K-Akt signaling pathway. The BaP-toxicological classifiers and the CBRS prognostic model could provide useful support for risk stratification and inform personalized therapeutic strategies for GC patients. Molecular docking results suggest that BaP exhibits relatively strong binding affinity with these key toxicological targets, potentially implicating their involvement in BaP-induced gastric cancer toxicity. Therefore, this study integrates multi-dimensional omics data with advanced machine learning algorithms to establish a comprehensive analytical framework for the toxicological effects of between BaP and GC, which transcends the limitations of traditional analyses and offers unprecedented insights and evidence chains for elucidating the pathogenesis of GC.

Oxygen-containing heterocycles were reviewed as privileged scaffolds that had driven recent advances in rational drug design and synthetic methodology. The manuscript synthesized literature (2015-2025) on oxadiazoles, coumarins, morpholines, pyrans, furans, benzofurans and chromones and summarized how these scaffolds were engineered to optimize potency, selectivity and CNS drug-like properties. Mechanistic analyses demonstrated that oxygen atoms and carbonyl or ether functionalities consistently mediated key hydrogen-bonding and π-interactions within the catalytic anionic site (CAS) and peripheral anionic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), rationalizing observed AChE/BChE SAR and dual-site binding. Representative medicinal chemistry campaigns were highlighted: coumarin and coumarin-hybrid series provided potent dual-site inhibitors; 1,2-oxadiazoles or 1,3,4-oxadiazoles produced sub to low-nanomolar AChE/BChE leads; morpholine-bearing scaffolds afforded favourable BBB permeability and mixed-type inhibition; and pyranone-carbamate hybrids delivered highly BChE-selective inhibitors with promising in vivo cognitive effects. Synthetic strategies (multicomponent reactions, metal-catalysed cyclizations and green/one-pot protocols) were reviewed and correlated with scaffold diversification and improved ADME profiles. The review concluded by identifying gaps limited unified docking/SAR databases and sparse translational safety data and proposed a workflow combining fragment-based design, dual-site targeting and early ADME profiling to accelerate lead optimisation toward clinically relevant cholinesterase modulators. This focused synthesis of structure activity, mechanism and synthetic access was intended to inform future heterocycle-centric programs against neurodegenerative targets.

Alzheimer's disease (AD), a multifactorial neurodegenerative disorder, remains a major cause of cognitive decline in the aging population. Current pharmacological interventions provide only symptomatic relief, highlighting the urgent need for novel therapeutic strategies capable of modifying disease progression. Coumarins, particularly 7-hydroxycoumarin and its synthetic derivatives, have attracted considerable interest due to their broad pharmacological potential, including cholinesterase inhibition, monoamine oxidase (MAO) inhibition, antioxidant, anti-amyloidogenic and metal-chelating activities. This review presents a comprehensive analysis of synthetic 7-hydroxycoumarin derivatives reported over the past 15 years as potential anti-Alzheimer agents, classifying them according to their actions on key pathological hallmarks of AD, such as acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), MAO-B, β-amyloid (Aβ) aggregation, oxidative stress and neuroinflammation. Structure-activity relationship (SAR) analysis reveals that substitutions at the 7-, 3- and 4-positions of the coumarin scaffold critically influence pharmacological potency and selectivity, with aromatic and alkyl amine substitutions generally enhancing enzyme inhibition and neuroprotective effects. Several derivatives exhibited sub-micromolar to nanomolar inhibitory activity against AChE and MAO-B, along with antioxidant and anti-Aβ aggregation properties, supporting their multifunctional behaviour. Overall, this review highlights the therapeutic promise of 7-hydroxycoumarin derivatives as multitarget-directed ligands (MTDLs) and provides valuable insights for the rational design of new lead compounds for Alzheimer's disease.

Prostate cancer exhibits complex transcriptional heterogeneity that underlies disease progression and therapeutic resistance. We developed an integrative omics-to-therapy pipeline to identify actionable biomarkers and screen drug candidates using a combination of single-cell transcriptomics, cheminformatics, and machine learning. Single-cell RNA sequencing (scRNA-seq) of prostate cancer tissues enabled fine-grained clustering and differential gene expression analysis across malignant and non-malignant cell populations. ASPM (Abnormal Spindle Microtubule Assembly) emerged as a statistically significant and cell-type-enriched biomarker associated with proliferative tumor phenotypes. We curated a library of drug-like molecules and developed Prosta-Omics, a supervised machine learning tool trained to predict Topological Polar Surface Area (TPSA) from molecular SMILES using a Random Forest model. High-ranking compounds as predicted by Prosta-Omics were docked against a 3D model of ASPM revealing multiple candidates with strong binding affinities and key interaction motifs. The drugs with higher docking score were subjected into molecular dynamics (MD) simulation and ADMET analysis. This integrative strategy highlights ASPM as a viable therapeutic target and introduces Prosta-Omics as a robust predictive platform bridging single-cell analytics with AI-driven drug discovery for precision oncology in prostate cancer.

Tau-protein aggregation is a central pathological feature of Alzheimer's disease, so blocking fibril growth is an attractive therapeutic goal. We curated a high-quality set of 289 literature IC₅₀ measurements for human-tau aggregation and trained a stacked-ensemble QSAR model (SVR + RF + XGB) that achieves fivefold CV Q² = 0.63, external R² = 0.57 and RMSE = 0.73 log-units. Applicability-domain analysis revealed no high-influence outliers in the calibration set, and a 5-nearest-neighbour density test confirmed that each of sixteen previously unreported 1,2,4-triazole-naphthalene derivatives (TND, TND-1…TND-15) lies in locally populated chemical space, albeit at the edge of the global domain. The model predicts pIC₅₀ = 6.75-7.53 (IC₅₀ ≈ 30-177 nM), nominating TND-9, TND-15 and TND-5 as top-ranked candidates based on predicted potency. Nearly all TNDs fall within the BBB window (MW ≈ 350-450 Da, TPSA < 90 Ų); most obey cLogP ≤ 5, and the few slightly above still map to the BOILED-Egg CNS-positive zone. Retrospective docking against phosphorylated-tau fibrils (PDB ID 6HRF) highlighted TND, TND-5 and TND-14 with sub-micromolar predicted affinity, forming key contacts in the microtubule-binding cleft. These docking results support binding plausibility rather than quantitative aggregation inhibition. TND-8, although highly ranked by docking, was deprioritised owing to low predicted GI absorption. Physicochemical and CNS-oriented ADMET filters further support developability of the top leads. The integrated workflow-combining rigorously validated QSAR, structure-based docking on the 6HRF polymorph and developability profiling-provides an open-source blueprint for tau-aggregation inhibitor discovery. Consensus ranking prioritises TND-5 for immediate in-silico follow-up, with TND, TND-14, TND-9 and TND-15 as secondary leads.

Tyrosinase is the key rate limiting enzyme that controls melanin production. A series of novel kojic acid-cinnamic acid hybrids (JP1~26) were synthesized as tyrosinase inhibitors. All compounds displayed potential anti-tyrosinase activity with IC₅₀ values of 0.51~1.53 µM, ~ 10-30 folds stronger than control kojic acid. Among them, the strongest inhibitor JP5 inhibited tyrosinase in a mixed-type. Fluorescence quenching, 3D fluorescence, and CD spectra revealed the binding characteristic of JP5 with tyrosinase. Molecular docking displayed their binding detail with catalytic site residues. In addition, JP5 also could inhibit intracellular tyrosinase activity, thence, inhibiting melanin production in B16 cells. Therefore, kojic acid-cinnamic acid hybrids could service as potential tyrosinase inhibitors.

Accurate prediction of drug metabolism and pharmacokinetics (ADMET) properties is crucial in drug discovery. Here, we present a novel approach to enhance ADMET property predictions using Cross-Aligned Multimodal Attention (CMA) mechanisms, pretrained models, and multimodal techniques. ADMET data is collected and processed using image processing, graph neural networks, and chemical fingerprinting. Pretrained models like GROVER and ResNet generate a multi-channel data format, and the CMA mechanism aligns and correlates the data modalities. Grad-CAM technology interprets the model's predictions, visually demonstrating the relationship between compound properties and fragments. Our ADMET property prediction server ( http://guolab.mpu.edu.mo/CMA ) implements the CMA-based model and a substantial language model for ADMET property prediction. The innovation lies in the integration of multimodal data, the application of pretrained models, and the development of cross-modal alignment. This approach improves the efficiency and accuracy of ADMET property predictions and opens new avenues for research in molecular science, particularly in drug design and evaluation.