ReviewFrontiers in pharmacology2022
Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.
Review in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Molecular recognition and induced dimerization of hnRNP A2/B1 truncations by G-quadruplex single strand DNA.Scientific reports · 2026Article
- Progress in the study of ion channel function, mechanisms, and mathematical modeling in Parkinson's disease.iScience · 2026Review
- Modulation of ion channels as emerging therapeutic targets in the treatment of diabetic neuropathy.EXCLI journal · 2026Review
- Applying Entropic Measures, Spectral Analysis, and EMD to Quantify Ion Channel Recordings: New Insights into Quercetin and Calcium Activation of BK Channels.Entropy (Basel, Switzerland) · 2025Article
- Dynamics and Interplay of the Binding Pockets in the Glycine Receptor.Journal of chemical information and modeling · 2025Article
- Modulation of calcium signaling and metabolic pathways in endothelial cells with magnetic fields.Nanoscale advances · 2024Article
- Machine Learning Methods for the Analysis of the Patch-Clamp Signals.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Potassium Ion Channels in Glioma: From Basic Knowledge into Therapeutic Applications.Membranes · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Registered trials
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