Some abused psychostimulant drugs, such as cocaine, block the uptake function of dopamine transporter (DAT) leading to their addictions, while some other DAT inhibitors do not possess addiction liability and can potentially be used to treat cocaine abuse. In the development of such DAT inhibitors, it is necessary to avoid off-target binding. In particular, the blockage of a potassium channel, human <i>ether-a-go-go</i> (hERG), often leads to the potentially lethal ventricular tachycardia. In this study, to facilitate the discovery and development of high-affinity DAT inhibitors with low hERG affinities, we established a counter screening platform for DAT and against hERG by combining machine learning based quantitative structure-activity relationship (QSAR) modeling, chemical synthesis, experimental validations, and molecular modeling and simulations. Our results show the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and experimental validations of a test set of DAT inhibitors. Further, the QSAR models based on subsets of the data according to experimental approaches used have prediction powers as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulation results identified the structural elements responsible for a pair of DAT inhibitors having opposite affinity trends at DAT and hERG, which can be leveraged for rational optimization of the lead DAT inhibitors with desired pharmacological properties.

Integrating machine learning and molecular modeling in developing DAT inhibitors with desired properties and reduced hERG activity

ACS Spring Meeting 2021

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