Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity

Quantitative structure–activity relationship (QSAR) modelling is a major tool employed in the prediction of various endpoints. However, current QSAR literature is missing a full understanding of the impact of quantum chemical calculation methods on the estimation of molecular descriptors and model performance. Here, we provide a comprehensive analysis of the quantitative effects of different geometry optimization methods (semi-empirical, ab initio Hartee-Fock and density functional theory) on the molecular descriptors. Using experimental binding affinity to human serum albumin (HSA) data, we comparatively investigated the influence of employing descriptors derived from three calculation methods on the QSAR models. We propose a 4-descriptor QSAR model in line with the OECD validation principles for the prediction of drug binding affinity to HSA (log KHSA) as a potential tool for drug development. We also confirm the prediction capability of the proposed model on a heterogeneous external set of chemicals. Furthermore, we recommend an activity-independent rational approach for the selection of geometry optimization method for an improved QSAR model development.定量构效关系（Quantitative structure–activity relationship, QSAR）建模是用于各类终点预测的核心工具之一。然而，当前QSAR研究领域尚未全面阐明量子化学计算方法对分子描述符估算及模型性能的影响机制。本研究针对不同几何优化方法（半经验（semi-empirical）法、从头算哈特里-福克（Hartee-Fock）法与密度泛函理论（density functional theory）法）对分子描述符的量化影响开展了全面分析。我们采用人血清白蛋白（human serum albumin, HSA）实验结合亲和力数据集，对比探究了三种计算方法衍生的描述符对QSAR模型的影响。基于经合组织（Organisation for Economic Co-operation and Development, OECD）验证原则，我们构建了一款包含4个描述符的QSAR模型，用于预测药物与HSA的结合亲和力（log KHSA），可作为药物开发的潜在辅助工具。此外，我们在一组异质性外部化学数据集上验证了所提模型的预测能力。最后，我们提出了一种不依赖活性的理性选择策略，用于遴选最优几何优化方法，以助力更优质的QSAR模型开发。