Development of synthetic chloride transporters using high-throughput screening and machine learning
Year: 2025
Type: article
Abstract: is the most important feature to predict transport ability, but it is not sufficient by itself. The best ML model was able to identify potential chloride transporters from the DrugBank database and the predictions were experimentally validated. These insights can provide other researchers with inspiration and guidelines to develop ever more potent chloride transporters.
Source: Digital Discovery
DOI: 10.1039/d5dd00140d
Authors: Surid Mohammad Chowdhury, Nada J. Daood, Katherine R. Lewis, Rayhanus Salam, Hao Zhu, Nathalie Busschaert
Institutions: Tulane University, Rowan University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design, G217782777, Mechanism-Driven Virtual Adverse Outcome Pathway Modeling for Hepatotoxicity, Improving the Properties of Optical Anion Sensors using Transmembrane Transporters and Extracting Agents, G8738732214
OpenAlex ID: W4413164151