首页> 外文会议>Genetic and Evolutionary Computation Conference(GECCO 2004) pt.2; 20040626-630; Seattle,WA(US) >Comparative Molecular Binding Energy Analysis of HIV-1 Protease Inhibitors Using Genetic Algorithm-Based Partial Least Squares Method
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Comparative Molecular Binding Energy Analysis of HIV-1 Protease Inhibitors Using Genetic Algorithm-Based Partial Least Squares Method

机译:基于遗传算法的偏最小二乘方法比较HIV-1蛋白酶抑制剂的分子结合能

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Comparative molecular binding energy analysis (COMBINE) is a helpful approach for estimation of binding affinity of congeneric ligands that bind to a common receptor. The essence of COMBINE is that the ligand-receptor interaction energies are decomposed into residue-based energy contributions, and then the partial least squares (PLS) analysis is applied to correlate energy features with biological activity. However, the predictive performance of PLS model drops with the increase of number of noisy variables. With regard to this problem genetic algorithm (GA) combined with PLS approach (GAPLS) for feature selection has demonstrated the improvement on the prediction and interpretation of model. Therefore, the purpose of this paper is to derive a more accurate and more efficient GAPLS in COMBINE by introducing a number of successive refinements.
机译:比较分子结合能分析(COMBINE)是一种有用的方法,可用于评估与常见受体结合的同类配体的结合亲和力。 COMBINE的本质是将配体-受体相互作用能分解为基于残基的能量贡献,然后应用偏最小二乘(PLS)分析将能量特征与生物活性相关联。但是,PLS模型的预测性能随噪声变量数量的增加而下降。针对此问题,遗传算法(GA)结合PLS方法(GAPLS)进行特征选择已经证明了对模型预测和解释的改进。因此,本文的目的是通过引入许多连续的改进来在COMBINE中获得更准确,更有效的GAPLS。

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