We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.

中文翻译：

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应用“特征值分析（EVANS）”方法建立定量结构药代动力学关系模型

我们提出了EigenValue ANalySis（EVANS），这是一种QSPR方法，无需进行比对步骤即可考虑手性分子对映体整体的3D分子信息。EVANS遵循复杂的分子建模协议，该协议从物理化学性质和3D结构的混合矩阵中生成正交特征值；这些特征值在QSPR分析中用作自变量。提出了EVANS形式主义，并将其用于在基准数据集上建立定量结构药代动力学关系（QSPKR）模型，用于三个关键PK参数：稳态分布体积（VDss），清除率（CL）和半衰期（t1 / 2）。通过将EVANS方法生成的特征值与多元线性回归（MLR）结合使用，建立了预测性QSPKR模型，随机森林（RF）和支持向量机（SVM）算法，并且观察到EVANS QSPKR模型与文献中已发表的工作同步。因此，我们将EVANS方法学作为一线预测工具，以在药物发现和开发中对化合物进行优先排序。