ArticleJournal of chemical theory and computation2015
Modeling molecular kinetics with tICA and the kernel trick.
Article in Journal of chemical theory and computation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Topological Analysis Reveals Multiple Pathways in Molecular Dynamics.Journal of chemical theory and computation · 2025Article
- Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.Nature computational science · 2025Article
- Application of Anomaly Detection to Identify Important Features of Protein Dynamics.ACS omega · 2025Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Toward Grid-Based Models for Molecular Association.Journal of chemical theory and computation · 2025Article
- Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Cooperative Membrane Binding of HIV-1 Matrix Proteins.The journal of physical chemistry. B · 2024Article
- How Robust Is the Ligand Binding Transition State?Journal of the American Chemical Society · 2023Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- Predicting rare events using neural networks and short-trajectory data.Journal of computational physics · 2023Article
- An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.Journal of chemical theory and computation · 2023Article
- EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.Briefings in bioinformatics · 2023Article
- EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.bioRxiv : the preprint server for biology · 2023Article
- Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles.The journal of physical chemistry. B · 2023Article
- Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation.Journal of chemical information and modeling · 2023Article
- Chasing collective variables using temporal data-driven strategies.QRB discovery · 2023Article
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2 authors.
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Abstract
The allure of a molecular dynamics simulation is that, given a sufficiently accurate force field, it can provide an atomic-level view of many interesting phenomena in biology. However, the result of a simulation is a large, high-dimensional time series that is difficult to interpret. Recent work has introduced the time-structure based Independent Components Analysis (tICA) method for analyzing MD, which attempts to find the slowest decorrelating linear functions of the molecular coordinates. This method has been used in conjunction with Markov State Models (MSMs) to provide estimates of the characteristic eigenprocesses contained in a simulation (e.g., protein folding, ligand binding). Here, we extend the tICA method using the kernel trick to arrive at nonlinear solutions. This is a substantial improvement as it allows for kernel-tICA (ktICA) to provide estimates of the characteristic eigenprocesses directly without building an MSM.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.