Fragment-based and classical quantitative structure–activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q (2) = 0.68 and r (2) = 0.72; HQSAR, q (2) = 0.63 and r (2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, [Formula: see text] ; classical QSAR, [Formula: see text]).     

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure–activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set \({R}^{2}= 0.904\); test set: \({Q}^{2}= 0.676\); whole data set: stability \(s = 0.803\)). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.     

基于支持向量机、支持向量回归和分子对接的CYP450 1A2抑制剂的发现研究

支持向量机,支持向量回归和分子对接的计算方法已广泛应用于化合物的药理活性计算。为了提高计算的准确性和可靠性,拟以细胞色素P450酶1A2为研究载体,运用建立的联合SVM-SVR-Docking计算模型预测潜在的CYP1A2抑制剂。其中,建立的最优SVM定性模型训练集,内部测试集和外部测试集的准确率分别为99.432%,97.727%和91.667%。最优SVR定量模型训练集和测试集的R和MSE分别为0.763,0.013和0.753,0.056。实验表明两个模型具有较高的准确性和可靠性。通过对SVM和SVR模型结果的比较分析,发现连接性指数、分子构成描述符和官能团数目等分子描述符可能与CYP1A2抑制剂的辨识和活性预测密切相关。随后利用分子对接技术分析化合物与CYP1A2的结合构象及相互作用的稳定性。形成氢键相互作用的关键氨基酸包括THR124,ASP320;形成疏水相互作用的关键氨基酸包括ALA317和GLY316。所获得模型可用于天然产物化学成分中CYP1A2潜在抑制剂的活性计算及其介导的药物-药物相互作用预测提供理论指导,也为合理联合用药提供一定参考。共获得20个对CYP1A2具有潜在抑制活性的化合物。部分结果与文献结果相互印证,进一步说明了模型的准确性和联合计算策略的可靠性.     

Megavariate analysis of environmental QSAR data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD)

This paper introduces principal component analysis (PCA), partial least squares projections to latent structures (PLS), and statistical molecular design (SMD) as useful tools in deriving multi- and megavariate quantitative structure-activity relationship (QSAR) models. Two QSAR data sets from the fields of environmental toxicology and environmental chemistry are worked out in detail, showing the benefits of PCA, PLS and SMD. PCA is useful when overviewing a data set and exploring relationships among compounds and relationships among variables. PLS is the regression extension of PCA and is used for establishing QSARs. SMD is essential for selecting informative training and test sets of compounds for QSAR calibration and validation.     

Active site fingerprinting and pharmacophore screening strategies for the identification of dual inhibitors of protein kinase C $$(\hbox {PKC}{\upbeta })$$ and poly (ADP-ribose) polymerase-1 (PARP-1)

Current clinical studies have revealed that diabetic complications are multifactorial disorders that target two or more pathways. The majority of drugs in clinical trial target aldose reductase and protein kinase C (\(\text {PKC}{\upbeta }\)), while recent studies disclosed a significant role played by poly (ADP-ribose) polymerase-1 (PARP-1). In light of this, the current study was aimed to identify novel dual inhibitors of \(\text {PKC}{\upbeta }\) and PARP-1 using a pharmaco-informatics methodology. Pharmacophore-based 3D QSAR models for these two targets were generated using HypoGen and used to screen three commercially available chemical databases to identify dual inhibitors of \(\text {PKC}{\upbeta }\) and PARP-1. Overall, 18 hits were obtained from the screening process; the hits were filtered based on their drug-like properties and predicted binding affinities (docking analysis). Important amino acid residues were predicted by developing a fingerprint of the active site using alanine-scanning mutagenesis and molecular dynamics. The stability of the complexes (18 hits with both proteins) and their final binding orientations were investigated using molecular dynamics simulations. Thus, novel hits have been predicted to have good binding affinities for \(\text {PKC}{\upbeta }\) and PARP-1 proteins, which could be further investigated for in vitro/in vivo activity.     

Docking and quantitative structure–activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase

We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC₅₀) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure–activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q ² = 0.796) and fitted correlation coefficient (R ² = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.