Molecular Diversity最新文献

Focal adhesion kinase 2 (FAK2) is a non-receptor tyrosine kinase that orchestrates key oncogenic processes, including cell adhesion, migration, proliferation, and survival, and is frequently overexpressed in multiple cancer types. Targeting its ATP-binding and active sites has emerged as a promising therapeutic approach. Here, we performed a systematic virtual screening of 11,699 phytoconstituents from the Indian medicinal plants to identify potent FAK2 inhibitors. Docking analysis shortlisted top candidates based on binding affinity, followed by pharmacokinetic profiling (ADMET) and biological activity prediction (PASS). Two compounds, Cucurbitacin S and Kammogenin, exhibited strong binding affinities (- 9.5 and - 9.3 kcal/mol) and favorable ADMET properties, with predicted anticancer and anti-inflammatory activities. Detailed interaction studies revealed stable binding to critical residues, including Asp549 in the active site. Molecular dynamics simulation for 300 ns confirmed the stability and compactness of FAK2-compound complexes, with minimal structural perturbation. Essential dynamics analyses indicated reduced conformational flexibility upon ligand binding, while MM-PBSA calculations demonstrated favorable binding free energies. A comparative analysis with the reference inhibitor PF-562271 indicated the therapeutic potential of both phytochemicals, pending experimental evaluation. These findings suggest that Cucurbitacin S and Kammogenin are promising lead scaffolds for the development of plant-derived FAK2 inhibitors; however, as this is a computational, hypothesis-generating study, the results warrant further experimental validation to confirm their therapeutic potential.

Vacuolar protein sorting 4 (VPS4) is an AAA-ATPase that mediates ESCRT-III disassembly critical for membrane remodeling events like autophagosome closure and endolysosomal repair. Aberrant expression of VPS4 is associated with cancer progression and poor prognosis, making VPS4 a potential anticancer target. To date, very few VPS4 inhibitors have been reported, therefore the identification and development of VPS4 inhibitors is urgently needed. In this study, we employed a multi-tiered structure based virtual screening strategy, molecular dynamic simulation accompanied by pharmacokinetic analysis and in vitro screening to identify novel inhibitors of VPS4. The identified inhibitor comp-23 effectively inhibited the enzymatic activity of VPS4B with an IC₅₀ value of 12.84 ± 2.51 µM. Protein ligand interaction profile and molecular dynamic simulation revealed the ATP binding residues such as Ala137, Gly177, Glu179, Asn279, and His313 were the main contributors to the binding of this compound. Comp-23 serves as a hit compound for further optimization to explore VPS4-related functions.

Natural products are main sources of new chemical entities for pesticide discovery and also important modification substrates for activity enhancement. In this study, a series of novel oleanolic acid derivatives containing piperazine pyrimidine structures were designed and synthesized. Multi NMR and high resolution-mass spectrometry technologies were utilized to characterize the synthesized compounds' structures. Antimicrobial potential of the title compounds was assessed against several phytopathogenic fungi and bacteria, namely Botryosphaeria dothidea, Fusarium sp. in morchella esculenta, Sclerotinia sclerotiorum, Fusarium graminearum, Rhizoctonia solani, Botrytis cinerea in strawberry, Botrytis cinerea in tobacco, Botrytis cinerea in blueberry, Alternaria solani, and Phomopsis sp., and Pseudomonas syringae pv. actinidiae, Xanthomonas oryzae pv. oryzicola, and Xanthomonas axonopodis pv. citri. The results showed that most of these compounds displayed good-to-moderate antimicrobial activities. Among them, compound 4e showed excellent efficacy against B. dothidea with EC₅₀ value of 7.39 μg/mL outperforming pyrimethanil (13.37 μg/mL). In vivo study further confirmed that 4e effectively reduced the incidence of postharvest soft rot in kiwifruit caused by B. dothidea. Mechanistic research revealed that 4e could disrupt cell integrity of B. dothidea causing protein, nucleic acid leakage, and over-generate reactive oxygen species. Furthermore, molecular docking simulation analysis suggested that 4e could readily bound to the isocitrate lyase protein. In addition, compound 4p exhibited promising antibacterial activity superior to the commercial bactericidal thiodiazole copper. The remarkable antimicrobial efficacy of the oleanolic acid derivatives provided great potential for preventing and curing plant diseases.

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.