ArticleJournal of chemical theory and computation2024
Markov State Models: To Optimize or Not to Optimize.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Simulation-Free Topological Basis for Building Compact Koopman Models of Protein Folding.Journal of chemical information and modeling · 2026Article
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- Reducing weighted ensemble variance with optimal trajectory management.The Journal of chemical physics · 2026Article
- Coarse-Graining Self-Assembly by the Stochastic Landscape Method.Journal of chemical theory and computation · 2025Article
- Residence time in drug discovery: current insights and future perspectives.Pharmacological reports : PR · 2025Review
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Ion Channel Reaction Networks: Dielectric Screening and the Importance of Off-Pathway Flux.Journal of chemical theory and computation · 2025Article
- Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.The Journal of chemical physics · 2025Article
- Article
- Kemeny Constant-Based Optimization of Network Clustering Using Graph Neural Networks.The journal of physical chemistry. B · 2024Article
- Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites.Journal of chemical theory and computation · 2024Article
- Interplay between phosphorylation and oligomerization tunes the conformational ensemble of SWEET transporters.bioRxiv : the preprint server for biology · 2024Article
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3 authors.
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Abstract
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.
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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.