Molecular Diversity最新文献

Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.

The ATP-binding cassette transporter superfamily plays a pivotal role in cellular detoxification and drug efflux. ATP-binding cassette subfamily G member 2 (ABCG2) referred to as the Breast cancer resistance protein has emerged as a key member involved in multidrug resistance displayed by cancer cells. Understanding the molecular basis of substrate and inhibitor recognition, and binding within the transmembrane domain of ABCG2 is crucial for the development of effective therapeutic strategies. Herein, utilizing state-of-the-art molecular docking algorithms and molecular dynamic (MD) simulations, molecular binding of substrates and inhibitors with ABCG2 are defined, distinctly. We performed extensive virtual screening of Drugbank to identify the potential candidates, and MD simulations of docked complexes were carried out in POPC lipid bilayer. Further, the binding affinities of compounds were estimated by free binding energy employing MM-GBSA. To gain deeper insight into the binding affinities and molecular characteristics contributing to inhibitory potential of certain substrates, we included some well-known inhibitors, like Imatinib, Tariquidar, and Ko 143, in our analysis. Docking results show three compounds, Docetaxel > Tariquidar > Tezacaftor having the highest binding affinities (≤ 12.00 kcal/mol) for ABCG2. Remarkably, MM-GBSA results suggest the most stable binding of Tariquidar with ABCG2 as compared to the other inhibitors. Furthermore, our results suggested that Docetaxel, Ozanimod, Pitavastatin, and Tezacaftor have the strongest affinity for the drug-binding site(s) of ABCG2. These results provide valuable insights into the key residues that may govern substrate/inhibitor recognition, shedding light on the molecular determinants influencing substrate specificity, transport kinetics, and ABCG2-mediated drug efflux.

SH2 (Src Homology 2) domains play a crucial role in phosphotyrosine-mediated signaling and have emerged as promising drug targets, particularly in cancer therapy. STAT3 (Signal Transducer and Activator of Transcription 3), which contains an SH2 domain, plays a pivotal role in cancer progression and immune evasion because it facilitates the dimerization of STAT3, which is essential for their activation and subsequent nuclear translocation. SH2 domain-mediated STAT3 inhibition disrupts this binding, reduces phosphorylation of STAT3, and impairs dimerization. This study employed an in silico approach to screen potential natural compounds that could target the SH2 domain of STAT3 and inhibit its function. The phytomolecules (182455) were retrieved from the ZINC 15 database and were docked using various modes like HTVS, SP, and XP. The phytomolecules exhibiting higher binding affinity were selected. MM-GBSA was performed to determine binding free energy, and the QikProp tool was utilized to assess the pharmacokinetic properties of potential hit compounds, narrowing down the list of candidates. Molecular dynamics simulations, thermal MM-GBSA, and WaterMap analysis were performed on compounds that exhibited favorable binding affinities and pharmacokinetic characteristics. Based on docking scores and binding interactions, ZINC255200449, ZINC299817570, ZINC31167114, and ZINC67910988 were identified as potential STAT3 inhibitors. ZINC67910988 demonstrated superior stability in molecular dynamics simulation and WaterMap analysis. Furthermore, DFT was performed to determine energetic and electronic properties, and HOMO and LUMO sites were predicted for electronic structure calculation. Additionally, network pharmacology was performed to map the compounds' interactions within biological networks, highlighting their multitarget potential. Compound-target networks elucidate the relationships between compounds and multiple targets, along with their associated pathways and help to minimize off-target effects. The identified lead compound showed strong potential as a STAT3 inhibitor, warranting further validation through in vitro and in vivo studies.

Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = - 80.35 ± 11.51 kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.

A series of novel isatin-oxime ether derivatives were designed, synthesized and characterized by ¹H NMR and ¹³C NMR and HRMS. These compounds were evaluated for their in vitro cytotoxicity against three human cancer cell lines (A549, HepG2 and Hela) by MTT assay. According to the experimental results, compounds 6a (IC₅₀ = 0.34μM), 6c (IC₅₀ = 14nM) and 6r (IC₅₀ = 45nM) were found as the excellent selectivity and high activity against A549, whereas compounds 6m (IC₅₀ = 12nM) and 6n (IC₅₀ = 25nM) displayed the significant activity for HepG2, respectively. Compound 6f (IC₅₀ = 30nM), 6n (IC₅₀ = 9nM) and 6o (IC₅₀ = 20nM) also showed the excellent activity against Hela. From the experiments of cell migration and colony formation assays, the findings demonstrated that 6m can effectively suppress the migration and growth of HepG2 cells. In addition, the results of molecular docking studies determined the strong binding interactions between the potential active compounds 6m and 6n and the active sites of isocitrate dehydrogenase 1 (IDH1) with the lowest binding affinity energy.

Src homology-2 domain-containing protein tyrosine phosphatase 1 (SHP-1) is a member of protein tyrosine phosphatase (PTP) family, and serves as a crucial negative regulator of various oncogenic signaling pathways. The development of SHP-1 agonists has garnered extensive research attention and is considered as a promising strategy for treating tumors. In this review, we comprehensively analyze the advancements of SHP-1 agonists, focusing on their structures and biological activities. Based on the structure skeletons, we classify these SHP-1 agonists as kinase inhibitors, sorafenib derivatives, obatoclax derivatives, lithocholic acid derivatives and thieno[2,3-b]quinoline derivatives. Additionally, we discuss the potential opportunities and challenges for developing SHP-1 agonists. It is hoped that this review will provide inspiring insights into the discovery of drugs targeting SHP-1.

Overexpressed AXL kinase is involved in various human malignancies, which incurs tumor progression, poor prognosis, and drug resistance. Suppression of the aberrant AXL axis with genetic tools or small-molecule inhibitors has achieved valid antitumor efficacies in both preclinical studies and clinical antitumor campaigns. Herein we will report the design, synthesis, and structure-activity relationship (SAR) exploration of a series of anilinopyrimidine type II AXL inhibitors. Among these inhibitors, 4l exhibited the enzymatic AXL and cellular BaF3/TEL-AXL IC₅₀ values of 0.5 nM and less than 0.2 nM, respectively. Western blot analysis displayed that 4l dose-dependently inhibited the phosphorylation of AXL and its downstream cascade Akt, which was better than that of the reference control R428. Moreover, 4l markedly suppressed the AXL/GAS6-mediated migration in NCI-H1299 cells.

Natural compounds' derivatives as lead structures could effectively solve plant disease problems. In this article, amide compounds and amide ester compounds were synthetized through ferulic acid as the parent nucleus structure, and their biological activities in vitro and in vivo were evaluated. Compound 1q was screened out as the one with the best activity performance toward Xanthomonas axonopodis pv. citri (Xac), which displayed the inhibition rate of 100% and the EC₅₀ as low as 4.56 μg/mL. The results of in vivo experiments on citrus leaves infected with Xac showed that compound 1q had a protective efficacy of 60.98% and a curative efficacy of 26.56%. The mechanism of action as well as molecular docking was previously studied using extracellular polysaccharide (EPS) content, bacterial membrane permeability, and scanning electron microscopy (SEM) observations. Experimental results show that compound 1q can become an antibacterial agent for preventing and managing plant diseases.

Pyridazine and pyridazinone belong to the same group of six-membered heterocyclic compounds, and both structurally feature two adjacent nitrogen atoms. Pyridazine and pyridazinone derivatives are frequently used as core structures in the development of new green agrochemicals due to their high activity and environmental friendliness, attracting significant attention from researchers in recent years. Over the past 20 years, significant developments have occurred in the field of pyridazine and pyridazinone derivatives, which exhibit insecticidal, fungicidal, herbicidal, antiviral, and plant growth regulating activities. Hence, summarizing the process of creating novel molecules with pyridazine and pyridazinone structures through design concepts, understanding structure-activity relationships, and mechanisms of action is an important undertaking. This review aims to provide a comprehensive overview of these advancements, shedding light on the discovery and mechanism of action of novel pesticides in the pyridazine and pyridazinone categories.

Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD₅₀, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity. The models employed molecular descriptors such as Morgan fingerprints, MACCS-166 keys, and Klekota-Roth, PubChem fingerprints integrated with machine learning algorithms including random forest, gradient boosting regressor, and support vector machine. The HDAC3 QSAR models achieved Q²_test values of up to 0.76 and RMSE values as low as 0.58, while toxicity models attained Q²_test values of 0.63 and RMSE values down to 0.41, with applicability domain (AD) coverage exceeding 68%. Internal validation by fivefold cross-validation (Q²cv = 0.70 for HDAC3 and 0.60 for toxicity) and y-randomization confirmed model reliability. Shapley additive explanation (SHAP) was also used to explain the influence of modeling features on model prediction results. The most predictive QSAR models are integrated into the developed HDAC3_VS_assistant application, which is freely available at https://hdac3-vs-assistant-v2.streamlit.app/ . Virtual screening conducted using the HDAC3_VS_assistant web application allowed us to reveal a number of potential inhibitors, and the nature of their bonds with the active HDAC3 site was additionally investigated by molecular docking.