AIMC Topic: Dipeptides

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AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.

Journal of chemical theory and computation May 13, 2025
Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) al...
Algorithms Molecular Dynamics Simulation Dipeptides Amyloid Precursor Protein Secretases
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Deep learning path-like collective variable for enhanced sampling molecular dynamics.

The Journal of chemical physics May 7, 2024
Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a numb...
Deep Learning Molecular Dynamics Simulation RNA Dipeptides Thermodynamics
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Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

The Journal of chemical physics Jul 14, 2023
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fi...
Neural Networks, Computer Machine Learning Water Molecular Dynamics Simulation Dipeptides
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Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI).

The Journal of chemical physics Apr 21, 2022
A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates usi...
Neural Networks, Computer Artificial Intelligence Isomerism Dipeptides Alanine
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics Aug 21, 2018
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...
Neural Networks, Computer Peptides Molecular Dynamics Simulation Dipeptides
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