Molecular Diversity最新文献

Meso-substituted A₂B corrole (C1) and A₂B₂ porphyrin (P1) having phenylaniline derivatives were developed. Their metal complexes with Gallium(III) were prepared and studied for anti-cancer applications in photo-dynamic therapy (PDT). Ga(III)-macrocycles displayed bright emission in red region (590-700 nm) and preferentially colocalized in the endoplasmic reticulum of the breast cancer cells. Both the Ga(III) macrocycles showed ROS generation ability in breast cancer cells with bright green fluorescence as judged by confocal microscopy. The Ga(III)corrole (Ga1) exhibited decent photo-cytotoxicity against breast cancer and triple negative breast cancer cells with IC₅₀ values of 9.6 ± 2.1 and 13.8 ± 1.2 µM, respectively. Ga(III)porphyrin (Ga2) displayed good photocytotoxicity (IC₅₀ 5.5 ± 0.8 µM) in combination therapy with autophagy inhibitor (chloroquine; CQ, 50 µM) suggesting it's autophagic behaviour. Ga(III) macrocycles were found to be non-toxic to the normal RPE1 cell line under dark and light conditions, implying that they can be advantageous for cancer diagnosis applications.

The scope of the Ugi four-center three-component reaction involving 145 oxocarboxylic acids, primary amines, and 220 isonitriles under parallel synthesis conditions was studied. Special attention was paid to the limitations of each starting material; in particular, the relative reactivity of various oxocarboxylic acid types was established. For a model validation library of 1000 members, an experimental synthesis success rate of 88% and median yield of 47% was achieved. The obtained results and established trends were used to generate a 363-million synthetically tractable virtual chemical space of γ- and δ-lactams. The distribution of physicochemical properties within this chemical space revealed that 43% of its members complied with the Lipinski rule-of-five, and a significant fraction of members (21.5 million) were lead-like. Furthermore, the chemical space showed low similarity to already existing compound collections and was enriched with disk-like molecules. Comparison with the ChEMBL database revealed that over 100 representatives generated had biological activity, with some exhibiting potency in the low nanomolar range.

Last decade grabbed tremendous scientific attention towards the novel synthetic strategies for the synthesis and derivatization of adamantane core. Adamantane, owing to its unique chemical structure and high biological activity, has always been a subject of perpetual interest for medicinal chemists. The current review deals with the elucidation of the traditional and conventional methods as well as the application of novel methodologies for synthesizing the adamantane derivatives incorporating THREE, FOUR, FIVE, and SIX heterocyclic nuclei. Consequently, medicinal chemists have focused their efforts on compounds containing adamantane-based heterocycles to identify new therapeutic agents for various biological activities. For novelty, by screening the previous literature survey, we found no previously reported brief survey on the chemical modification of adamantane, especially the adamantane-based heterocyclic nuclei. In addition, the review attempts to inform researchers of how adamantane connected with different classified heterocyclic compounds, which incorporate either nitrogen, oxygen, and sulfur atoms, or combined two or all hetero atoms, as well as their engagement in diverse biological activities. Finally, we envision that the current review will successfully engage researchers in discovering novel, promising, simple materials for developing new various biological activities and drugs.

Psoriasis is a common polygenic hereditary skin disease and difficult to cure. Vasicine is an active alkaloid from Chinese herbal medicine Justicia adhatoda L. In present work, we have prepared a series of vasicine derivatives, and anti-inflammatory activities in vitro and in vivo have been evaluated. The in vitro results revealed that derivatives showed good anti-inflammatory activity of inhibiting NO generation. In vivo studies indicated that 4r could alleviate imiquimod induced skin inflammation, reduce the thickness of the epidermis and pathological lesions. Mechanism research showed that 4r could attenuate psoriasis-like skin inflammation via inhibiting MAPK signaling pathway activation, and alleviate LPS-induced inflammation in HaCat cells. Molecular docking study demonstrated that 4r could effectively bind to the active pocket of target MAPK protein 4U3Y and 5J5T. Therefore, vasicine derivatives may be considered as potent MAPK inhibitors for psoriasis treatment.

Nitroaromatic small molecules are established anti-infectives, including against trypanosomal diseases. Inspired by our previously identified suicide inhibitors of Mycobacterium tuberculosis (Mtb) decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1), we report the synthesis and in vitro antitrypanosomal activity of six novel nitroquinoline derivatives (4a-4f), as well as the antitrypanosomal activity of 13 previously described nitroquinolone anti-Mtb compounds, 8a-8 m. Two compounds exhibited sub-micromolar activity (EC₅₀ = 0.3-0.5 µM), while thirteen compounds exhibited low micromolar activity (EC₅₀ = 1.1-8.0 µM) against Trypanosoma brucei. This study highlights nitroquinolones and nitroquinolines as a source of compounds that exhibit both antitrypanosomal and antitubercular activities.

Increasing concern about highly pathogenic avian influenza A (H5N1) is prompting the development of new antivirals directed toward conserved viral entities that are resistant to mutational escape. Here, at a multi-scale and precision-guided computational level, we employed a set of procedures to identify potential small-molecule inhibitors of the influenza virus PA endonuclease, a central component of the viral RNA polymerase complex responsible for cap-snatching of mRNA transcription. Through the structurally diverse drug-like dataset, we initiated structure-based virtual screens against the PA catalytic domain and received 1,500 high-affinity candidates. Top-scoring candidates were optimized using quantum mechanical density functional theory (DFT) computations and electron reactivity/orbital distribution analyses. Through re-docking of optimized geometries using DFT, lead molecules were subjected to exhaustive 1-microsecond molecular dynamics (MD) simulations and MM/GBSA binding free energy decomposition and principal component analysis (PCA) sampling of dynamic conformational topographies. Free energy surface mapping of low-energy basins and superimposition validation of pose stabilities verified sub-angstrom deviations. Significantly, 24782939 registered the least thermodynamic profile (ΔG = -45.8 kcal/mol), greatest H-bond persistence, and computed pIC₅₀ of 8.17 using a machine-learned predictive model trained against structurally diverse chemical scaffolds. This multi-scale, integrated framework, involving atomic, energetic, and predictive scales, holds promise for translational applications of computational pipelines in antiviral discovery. Our findings nominate 24,782,939 as a highly promising inhibitor of PA endonuclease and have the potential to be developed into a next-gen therapeutic candidate against influenza A viruses.

Protein tyrosine phosphatase 1B (PTP1B) is a key therapeutic target for diabetes, obesity, and cancer. However, the development of its inhibitors faces challenges including low selectivity and poor bioavailability. Although deep learning (DL) can accelerate drug discovery, prior models often overlooked structural distinctions between non-natural products (NNPs) and natural products (NPs) in chemical datasets. In this study, we separated PTP1B inhibitors and decoys into NPs and NNPs subsets to build activity prediction models tailored to their respective chemical spaces. Using transfer learning (TL), we enhanced model performance specifically for NPs. Five-fold cross-validation was used for hyperparameter optimization and for evaluating the activity prediction performance of the three model architectures. The results showed that Attentive FP (AFP) performed best among graph neural networks, Extended-Connectivity Fingerprints 4 (ECFP4) led in multi-layer perceptron (MLP) models using molecular fingerprints, and PubChem10M_SMILES_BPE_450k (P10M) excelled among SMILES-based Transformers. The new models for NPs, derived from the three model architectures via TL (pre-trained on NNPs then fine-tuned on NPs), all outperformed their original counterparts. Random splitting further confirmed the enhancing effect of TL on NPs activity prediction and the generalization ability of models. We also developed a web platform ( http://ptp1bpredict.top ) that allows for the independent use of the AFP, MLP-ECFP4, and P10M models, including their transfer-learned variants, to predict PTP1B inhibition by NNPs and NPs. In summary, this work provides a novel strategy for DL-based screening of PTP1B inhibitors.

Drug-target interaction (DTI) prediction is crucial for drug discovery. Deep learning has been extensively utilized to reduce costs and expedite this process. However, most existing methods employ a two-channel architecture that separately constructs feature extraction networks for the drug and the target. These approaches fail to fully leverage the original input data and are unable to completely learn the features from it. In this study, we propose DeepMCL-DTI, an attention-based multi-channel deep learning model with four feature extraction channels: Graph Sample and Aggregate and convolutional neural network for drug features, and ProtBert and bidirectional convolutional long short-term memory for protein features. An interact-attention module models drug-target interactions across both spatial and channel dimensions. Extensive experiments conducted on the DrugBank and Davis datasets demonstrate that DeepMCL-DTI outperforms state-of-the-art methods. A case study on the angiotensin-converting enzyme 2 receptor further confirms its effectiveness as a pre-screening tool for drug discovery.

Carbon disulfide (CS₂) exhibits unique chemical properties and multifunctional reactivity that render it indispensable in the synthesis of diverse organic molecules. Despite its simple structure, CS₂ exhibits unique chemical properties that have established it as a versatile and indispensable reagent in organic synthesis. The electrophilic nature of the carbon center, combined with the nucleophilicity of the sulfur atoms, enables CS₂ to participate in a wide array of reactions, making it a key sulfur source for the introduction of sulfur functionalities into organic molecules. The ambident nature of CS₂ allows it to interact effectively with a range of nucleophiles-including amines, thiols, and organometallic reagents-leading to key intermediates such as dithiocarbamates and xanthates. Its electrophilic carbon flanked by nucleophilic sulfurs facilitates diverse synthetic pathways, encompassing nucleophilic substitution, addition across unsaturated bonds, cycloaddition, and transition-metal-catalyzed cross-coupling reactions. These transformations have significantly advanced the efficient synthesis of sulfur-rich compounds, including dithiocarbonates, thioesters, thioketones, and various sulfur heterocycles. This review delineates the pivotal role of CS₂ in contemporary organic synthesis.

The rising incidence of type 2 diabetes mellitus (T2DM) and the declining approval rate of new antidiabetic drugs highlight an urgent need for safer and more effective therapeutics. Partial PPARγ agonists have emerged as promising alternatives, offering efficacy with reduced side effects than full agonists. This study employed a comprehensive in silico strategy, combining ligand-based pharmacophore modeling, 3D-QSAR, ADME pre-filtering, virtual screening, molecular docking, MM-GBSA binding energy calculations, MD simulations, DFT analysis and toxicity predictions. A validated six-feature pharmacophore and robust 3D-QSAR model were developed from 71 known PPARγ agonists with reported antidiabetic activity. Virtual screening of ~ 600,000 ZINC compounds identified four promising hits, CHEMBL1825121, CHEMBL4642973, CHEMBL4569907, and CHEMBL294165. These compounds showed superior docking scores (- 10.919 to - 10.386 kcal/mol) and MM-GBSA energies (- 85.9 to - 63.96 kcal/mol) compared to the internal ligand SR145 (- 10.351 kcal/mol, - 85.63 kcal/mol) and standard drugs; rosiglitazone (- 7.272 kcal/mol, - 48.14 kcal/mol) and pioglitazone (- 7.033 kcal/mol, - 47.21 kcal/mol). Detailed docking analysis revealed key interactions with Arg288, Ser342, and Glu343, consistent with partial agonism, while avoiding strong AF-2 helix stabilization associated with full activation. MD simulations confirmed the stability of the ligand-PPARγ complexes over 500 ns, while DFT analysis revealed favorable electronic and chemical reactivity profiles. Among the four identified hits, CHEMBL1825121 and CHEMBL4569907 were identified as the top candidates, displaying strong binding affinity, high structural stability and favorable pharmacokinetic properties. While experimental validation remains essential, these findings provide a rational strategy for the development of next-generation PPARγ modulators for T2DM.