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Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach
- Source :
- J Chem Inf Model
- Publication Year :
- 2021
- Publisher :
- American Chemical Society (ACS), 2021.
-
Abstract
- 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.
- Subjects :
- Models, Molecular
Quantitative structure–activity relationship
Molecular model
General Chemical Engineering
hERG
Library and Information Sciences
Machine learning
computer.software_genre
Ether
Article
Reuptake
Machine Learning
03 medical and health sciences
0302 clinical medicine
Cocaine
Dopamine
medicine
Humans
030304 developmental biology
Dopamine transporter
Dopamine Plasma Membrane Transport Proteins
0303 health sciences
biology
business.industry
Mechanism (biology)
Chemistry
food and beverages
General Chemistry
Potassium channel
3. Good health
Computer Science Applications
biology.protein
Artificial intelligence
business
computer
030217 neurology & neurosurgery
medicine.drug
Subjects
Details
- ISSN :
- 1549960X and 15499596
- Volume :
- 61
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Information and Modeling
- Accession number :
- edsair.doi.dedup.....2210ef7eba1109d021c6032ff145c0da