Psychostimulant drugs, such as cocaine, inhibit dopamine reuptake via blockading the dopamine transporter (DAT), which is the primary mechanism underpinning their abuse. Atypical DAT inhibitors are dissimilar to cocaine and can block cocaine- or methamphetamine-induced behaviors, supporting their development as part of a treatment regimen for psychostimulant use disorders. When developing these atypical DAT inhibitors as medications, it is necessary to avoid off-target binding that can produce unwanted side effects or toxicities. In particular, the blockade of a potassium channel, human ether-a-go-go (hERG), can lead to potentially lethal ventricular tachycardia. In this study, we established a counter screening platform for DAT and against hERG binding by combining machine learning-based quantitative structure–activity relationship (QSAR) modeling, experimental validation, and molecular modeling and simulations. Our results show that the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and pharmacological evaluation of a validation set of DAT inhibitors. Furthermore, the QSAR models based on subsets of the data according to experimental approaches used have predictive power as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulations identified the structural elements responsible for a pair of DAT inhibitors having opposite binding affinity trends at DAT and hERG, which can be leveraged for rational optimization of lead atypical DAT inhibitors with desired pharmacological properties.

中文翻译：

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通过结合机器学习和分子建模方法降低 DAT 抑制剂的 hERG 亲和力

精神兴奋药物，如可卡因，通过阻断多巴胺转运蛋白 (DAT) 来抑制多巴胺再摄取，这是支持其滥用的主要机制。非典型 DAT 抑制剂与可卡因不同，可以阻止可卡因或甲基苯丙胺诱导的行为，支持它们作为精神兴奋剂使用障碍治疗方案的一部分的发展。在开发这些非典型 DAT 抑制剂作为药物时，有必要避免可能产生不良副作用或毒性的脱靶结合。特别是，阻断钾通道，人类ether-a-go-go(hERG)，可导致潜在的致命性室性心动过速。在这项研究中，我们通过结合基于机器学习的定量构效关系 (QSAR) 建模、实验验证以及分子建模和模拟，建立了 DAT 和针对 hERG 结合的反筛选平台。我们的结果表明，可用数据足以建立稳健的 QSAR 模型，并通过 DAT 抑制剂验证集的化学合成和药理学评估进行验证。此外，根据所使用的实验方法，基于数据子集的 QSAR 模型也具有预测能力，这为靶向蛋白质的特定功能状态打开了大门。作为补充，