ArticleJournal of chemical theory and computation2021
Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- Entangled Schrödinger Bridge Matching.ArXiv · 2025Article
- Reaction Coordinates Are Optimal Channels of Energy Flow.Annual review of physical chemistry · 2025Review
- Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Effective data-driven collective variables for free energy calculations from metadynamics of paths.PNAS nexus · 2024Article
- Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.Journal of chemical theory and computation · 2024Article
- Exploring the hypolipidemic effects of bergenin from Saxifraga melanocentra Franch: mechanistic insights and potential for hyperlipidemia treatment.Lipids in health and disease · 2023Article
- OneOPES, a Combined Enhanced Sampling Method to Rule Them All.Journal of chemical theory and computation · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Reaction Coordinates for Conformational Transitions Using Linear Discriminant Analysis on Positions.Journal of chemical theory and computation · 2023Article
- Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.Journal of chemical theory and computation · 2023Article
- 3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.Molecules (Basel, Switzerland) · 2022Article
- Force-Field-Dependent DNA Breathing Dynamics: A Case Study of Hoogsteen Base Pairing in A6-DNA.Journal of chemical information and modeling · 2022Article
- Sequence dependence of transient Hoogsteen base pairing in DNA.PLoS computational biology · 2022Article
- Chemistrees: Data-Driven Identification of Reaction PathwaysJournal of chemical theory and computation · 2021Article
- Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.Journal of chemical theory and computation · 2021Article
- Collective variable-based enhanced sampling and machine learning.The European physical journal. B · 2021Review
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3 authors.
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Abstract
With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitions, a condensed set of descriptors, or collective variables (CVs), is needed to discern the relevant dynamics that describes the molecular process of interest. However, proposing an adequate set of CVs that can capture the intrinsic reaction coordinate of the molecular transition is often extremely difficult. Here, we present a framework to find an optimal set of CVs from a pool of candidates using a combination of artificial neural networks and genetic algorithms. The approach effectively replaces the encoder of an autoencoder network with genes to represent the latent space, i.e., the CVs. Given a selection of CVs as input, the network is trained to recover the atom coordinates underlying the CV values at points along the transition. The network performance is used as an estimator of the fitness of the input CVs. Two genetic algorithms optimize the CV selection and the neural network architecture. The successful retrieval of optimal CVs by this framework is illustrated at the hand of two case studies: the well-known conformational change in the alanine dipeptide molecule and the more intricate transition of a base pair in B-DNA from the classic Watson-Crick pairing to the alternative Hoogsteen pairing. Key advantages of our framework include the following: optimal interpretable CVs, avoiding costly calculation of committor or time-correlation functions, and automatic hyperparameter optimization. In addition, we show that applying a time-delay between the network input and output allows for enhanced selection of slow variables. Moreover, the network can also be used to generate molecular configurations of unexplored microstates, for example, for augmentation of the simulation data.
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