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《Dental materials》2022,38(2):333-346
ObjectiveAcrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches.MethodsA regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness.ResultsThe performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers.SignificanceThis study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.  相似文献   
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《Drug discovery today》2022,27(2):529-537
Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure–activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.  相似文献   
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EGFR is a well‐established therapeutic target of clinical relevance in cancer. However, acquisition of secondary mutation (T790M) makes first‐generation inhibitors ineffective. Therefore, to circumvent the problem of resistance, new T790M/L858R (TMLR) double mutant inhibitors are required. In this study, fragment‐based QSAR models (GQSAR) were generated for pyridinylimidazole derivatives having biological activity against TMLR mutants. The GQSAR model developed using partial least squares regression via stepwise forward–backward variable selection technique showed best results as judged using statistical parameters (r2, q2, and pred_r2). Additionally, applicability domain of the model was verified using Williams plot, which indicated that the predicted data are reliable. The GQSAR provided site‐specific clues wherein modifications related to decreasing lipophilic character and rotatable bonds and increasing SaaCHE‐index are required for improving inhibitory activity. Overall, the study indicated that the presence of acrylamide at R5 is essential for covalent bond formation with Cys797 and occurrence of aromatic residue at R2 is required for occupying hydrophobic region next to Met790 gatekeeper residue. Based on this information, new derivatives were designed that show better inhibitory activity than the experimentally reported most active molecules. Thus, the model developed can be used to design new pyridinylimidazole derivatives with improved TMLR bioactivity.  相似文献   
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Protein modification can have far‐reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high‐throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over‐NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high‐throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high‐throughput screening improved hit rates over traditional high‐throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.  相似文献   
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Sixteen novel coumarin‐based compounds are reported as potent acetylcholinesterase (AChE) inhibitors. The most active compound in this series, 5a (IC50 0.04 ± 0.01 µM), noncompetitively inhibited AChE with a higher potency than tacrine and galantamine. Compounds 5d , 5j , and 5 m showed a moderate antilipid peroxidation activity. The compounds showed cytotoxicity in the same range as the standard drugs in HEK‐293 cells. Molecular docking demonstrated that 5a acted as a dual binding site inhibitor. The coumarin moiety occupied the peripheral anionic site and showed π‐π interaction with Trp278. The tertiary amino group displayed significant cation‐π interaction with Phe329. The aromatic group showed π‐π interaction with Trp83 at the catalytic anionic site. The long chain of methylene lay along the gorge interacting with Phe330 via hydrophobic interaction. Molecular docking was applied to postulate the selectivity toward AChE of 5a in comparison with donepezil and tacrine. Structural insights into the selectivity of the coumarin derivatives toward huAChE were explored by molecular docking and 3D QSAR and molecular dynamics simulation for 20 ns. ADMET analysis suggested that the 2‐(2‐oxo‐2H‐chromen‐4‐yl)acetamides showed a good pharmacokinetic profile and no hepatotoxicity. These coumarin derivatives showed high potential for further development as anti‐Alzheimer agents.  相似文献   
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白雅婷  南楠  尹婕 《中国药事》2019,33(10):1174-1180
随着定量构效关系(QSAR)模型从二维至多维的不断发展,它被越来越广泛地应用于各个领域,同时也为药品的监管提供了新的参考方案。QSAR模型对于杂质的毒性预测可应用于药品的研发和质量控制过程,有利于控制药品的安全风险,缩减企业的研发成本。QSAR模型的应用程序也在不断优化,以确保QSAR模型可以不断地适用于新药及未知杂质的毒性研究。本文从QSAR模型的建模基础及发展历程出发,对近年来该模型在药物及其杂质毒性预测方面的应用研究报道进行了归纳总结。  相似文献   
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Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose–response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high‐quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter‐relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose‐informed random forest/hidden Markov model was superior to the dose‐naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose‐informed hidden Markov model strongly reduced " false‐negatives" (i.e. extreme sensitizers as non‐sensitizer) on all data sets. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   
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In the past decade, the discovery, synthesis, and evaluation for hundreds of CD38 covalent and non‐covalent inhibitors has been reported sequentially by our group and partners; however, a systematic structure‐based guidance is still lacking for rational design of CD38 inhibitor. Here, we carried out a comparative analysis of pharmacophore features and quantitative structure–activity relationships for CD38 inhibitors. The results uncover that the essential interactions between key residues and covalent/non‐covalent CD38 inhibitors include (i) hydrogen bond and hydrophobic interactions with residues Glu226 and Trp125, (ii) electrostatic or hydrogen bond interaction with the positively charged residue Arg127 region, and (iii) the hydrophobic interaction with residue Trp189. For covalent inhibitors, besides the covalent effect with residue Glu226, the electrostatic interaction with residue Arg127 is also necessary, while another hydrogen/non‐bonded interaction with residues Trp125 and Trp189 can also be detected. By means of the SYBYL multifit alignment function, the best CoMFA and CoMSIA with CD38 covalent inhibitors presented cross‐validated correlation coefficient values (q2) of 0.564 and 0.571, and non‐cross‐validated values (r2) of 0.967 and 0.971, respectively. The CD38 non‐covalent inhibitors can be classified into five groups according to their chemical scaffolds, and the residues Glu226, Trp189, and Trp125 are indispensable for those non‐covalent inhibitors binding to CD38, while the residues Ser126, Arg127, Asp155, Thr221, and Phe222 are also important. The best CoMFA and CoMSIA with the F12 analogues presented cross‐validated correlation coefficient values (q2) of 0.469 and 0.454, and non‐cross‐validated values (r2) of 0.814 and 0.819, respectively.  相似文献   
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