Lee Kuo Hao, Won Sung Joon, Oyinloye Precious, Shi Lei
Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse - Intramural Research Program, National Institutes of Health, Baltimore, MD, USA.
Adv Neurobiol. 2025;46:63-94. doi: 10.1007/978-3-031-96364-3_3.
The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training data sets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training data sets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.
多巴胺转运体（DAT）在中枢神经系统中起着关键作用，并与多种精神疾病有关。基于配体的方法有助于解读DAT配体的构效关系（SAR），尤其是定量构效关系（QSAR）建模。通过收集和分析来自文献及数据库的数据，我们系统地汇集了与DAT结合的各种不同配体，旨在识别DAT配体的一般特征，并揭示潜在新型DAT配体支架的化学空间。DAT药理活性数据的汇总，特别是来自ChEMBL等数据库的数据，为构建可靠的QSAR模型奠定了基础。对这些数据进行汇编和精细筛选，建立具有药理学测定和数据类型特定划分的高质量训练数据集，以及应用QSAR建模，被证明是探索相关化学空间的一种有前景的策略。通过使用来自不同版本ChEMBL的训练数据集对DAT QSAR模型进行系统比较，我们强调了提高数据集质量和增加数据集大小对DAT QSAR模型预测能力的积极影响。
