Molecular Diversity最新文献

Dengue fever is a mosquito-borne viral infection caused by dengue virus (DENV). It has emerged as a worldwide health problem, afflicting millions of people each year throughout the tropical and subtropical regions. To date, there is no FDA-approved drug for the treatment of dengue fever, highlighting the urgent need to discover novel anti-dengue drugs. In this study, multiple machine learning models were constructed to predict the inhibitory activity of small molecules against DENV NS2B-NS3, a protease that is crucial for the replication of DENV. Among them, RF-ECFP and XGBoost-ECFP were identified as the optimal models. The SHapley Additive exPlanations method was introduced for the interpretation of predictive results. Following the initial machine learning predictions, a multi-step screening process including multi-level molecular docking, molecular dynamics simulations, and molecular orbital calculations was conducted, ultimately identifying six hit compounds from a library containing ten million small molecules. Molecular docking indicated that compound 5 could form stable interactions with the catalytic triad in the active site of DENV NS2B-NS3 protease. The surface plasmon resonance and enzymatic inhibition assays further revealed that compound 5 exhibited a K_d value of 31.2 µM and an IC₅₀ value of 31.44 µM against the DENV NS2B-NS3 protease. Our work suggests that compound 5 represents a potential lead compound for the further development of DENV NS2B-NS3 protease inhibitors.

The urgent global health threat of antimicrobial resistance demands innovative therapeutic strategies. Herein, we report the design, synthesis, and biological evaluation (antibacterial and antifungal activities) of two series of alkynyl-linked aminoguanidine derivatives. A critical structure-activity relationship (SAR) was revealed: the exposure of the aminoguanidine group is paramount for activity. In vitro antibacterial and antifungal activities revealed that the imidazol-2-hydrazine series exhibited inhibition against both Gram-positive and Gram-negative bacteria, with minimum inhibitory concentration (MIC) ranging from 2 to 64 μg/mL. Among them, compound IIh exhibited a MIC of 2 μg/mL against both S. aureus CMCC 25923 and Enterococcus faecalis CMCC 29212, and also demonstrated significant antifungal activity against Candida albicans SC5314 with a MIC of 2 μg/mL. Time-kill kinetics established the rapid bactericidal nature of IIh, achieving complete eradication of E. coli and S. aureus within 1-2 h. Furthermore, IIh significantly inhibited biofilm formation and compromised bacterial membrane integrity, leading to the leakage of cytoplasmic proteins and nucleic acids. Checkerboard assays revealed a synergistic relationship between IIh and conventional antibiotics, reducing their effective MICs. As a promising candidate for combating resistant infections, IIh deserves further efficacy and safety studies.

Biomedical imaging has become an essential tool in the field of early cancer diagnosis and treatment. It is of great significance to develop a method that can monitor tumor hypoxia in real time and maintain a good photodynamic therapy (PDT) effect under hypoxic conditions. To achieve this goal, here we designed and synthesized a novel water-soluble porphyrin derivative, PtTSPP. The photophysical properties of PtTSPP were comprehensively characterized by UV-Vis absorption spectroscopy and fluorescence emission. This porphyrin has an extremely large Stokes shift and red emission (λ_em = 690 nm), which can effectively reduce the interference of background fluorescence and is used for the fluorescence imaging of HeLa cells. PtTSPP has good water solubility, is not prone to aggregation, and has a high singlet oxygen quantum yield. It also exhibits low dark toxicity and excellent photocytotoxicity. These properties make PtTSPP a promising candidate reagent for biomedical imaging and photodynamic therapy.

Multicomponent reactions (MCRs) are one-pot processes in which at least three reactants are combined to assemble a novel target product by intrinsic molecular diversity, optimal atom economy, and high efficiency. The development and design of new MCRs that yield valuable chemical products represent a major focus in organic chemistry. Meldrum's acid is a distinctive β-keto ester that is crucial in organic synthesis, especially in MCRs. The C5 position of this molecule demonstrates significant reactivity towards electrophilic substitution, whereas the carbonyl centers at C4 and C6 are notably vulnerable to nucleophilic attack. Its dual characteristics as both a nucleophile and electrophile, along with its tendency for enolization and decarboxylation, render it an essential synthon for the effective assembly of various heterocyclic scaffolds and acyclic organic compounds. In parallel, isocyanides have attracted significant attention as a versatile building block in MCRs because of their unique ambident reactivity. The combination of Meldrum's acid with isocyanide has opened a powerful avenue in diversity-oriented synthesis, leading to the discovery of unexpected products such as succinimide, carboxamide, imino-furopyranones, cyclopenta[b] pyridines, benzodiazepines, benzooxazepines, amidodiesters, functionalized triamides, and enamides. In continuation of our earlier review on Meldrum's acid and isocyanide-based MCRs, this minireview covers an in-depth discussion of this field, especially focusing on the most recent results from 2015 to 2025.

Developmental neurotoxicity (DNT) is linked to chemical exposure that disrupts the nervous system in humans or animals. Traditional methods for assessing chemical toxicity are valuable but often time-consuming, costly, and involve significant animal use, making it impractical to meet growing demands. To address this, we developed a deep learning-enhanced QSAR modeling framework aimed at predicting binding affinities towards molecular initiating events (MIEs) and key events (KEs) within the Adverse Outcome Pathway (AOP) relevant to exposure to pesticide-contaminated cannabis. Our model was trained on data from 24,476 compounds, sourced from the ChEMBL database, and tested against 4 MIE and 6 KE tasks. The DNNs showed superior performance, with an average correlation coefficient of 0.82 ± 0.05 and a root mean square error of 0.72 ± 0.08 for the test set. To enhance interpretability, we used SHAP values to explain the model's predictions clearly. Furthermore, ECFP4 feature contributions were mapped onto known neurotoxic compounds to highlight regions likely responsible for MIEs visually. Our results confirm that developed models accurately predict DNT and effectively identify the correct MIEs and KEs for several neurotoxicants.