HAL (France)
open access
Revealing intermediate states of voltage-gated potassium channels with physics-informed deep learning: application to Kv7.1
Introduction Voltage-gated potassium channels translate membrane-voltage changes into pore opening through coordinated conformational rearrangements, yet the intermediate states connecting these events remain difficult to capture. Revealing such hidden intermediates is essential for reconstructing p
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling.#156893
- Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification.#156883
- Deletion of the Kv2.1 delayed rectifier potassium channel leads to neuronal and behavioral hyperexcitability#1263435
- Multiscale Interpretable Deep-Learning Framework for the Identification and Visualization of Deformation Stages in Molecular Dynamics Trajectory#1015798
- Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials#653988
Record · ID 1266348
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive