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Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.  相似文献   

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In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class models. A comparison between the performance of GA–CG–SVM models and that of GA–SVM models shows that the SVM parameter optimization has a significant impact on the quality of SVM classification model. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Hui Zhang and Ming-Li Xiang are contributed equally.  相似文献   

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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.  相似文献   

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Experimental data is essential for the improvement of combustion kinetic models. Experimental design based on model analysis results can screen optimal experimental conditions with maximum information content. However, the computational cost of designing experiments by enumeration becomes unaffordable when an enormity of conditions with different temperatures/pressures/mixtures are to be investigated. An approach to facilitate the efficient discovery of optimal experimental conditions based on the genetic algorithm (GA) is proposed in this work. This approach regards the task of experimental design as an optimization problem to minimize an objective function that measures the information content provided by an experiment. The sensitivity entropy and surrogate model similarity are combined to form the objective function of optimization. Three designs of dimethyl ether experiments are provided to demonstrate the approach. The first case utilizes a benchmark for optimal experiments to validate the effectiveness of GA. The results show that GA can achieve better design results than the traditional enumeration strategy with less than 10% computational cost. The second case illustrates how GA is applied in the design of multiple experiments. The last one is an application in designing multiple experiments of various types, including ignition, species measurements in a jet-stirred reactor (JSR) and a plug flow reactor (PFR). The model parameters are calibrated with the designed experimental data using a Bayesian-based optimization approach. The uncertainties of model parameters are significantly reduced after the optimization.  相似文献   

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In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging, etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model are compared with other meta-models generated with neural network and response surface methodology.  相似文献   

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