Flow Matching for Optimal Reaction Coordinates of Biomolecular Systems DOI
Mingyuan Zhang, Zhicheng Zhang, Hao Wu

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and decomposability, which we reformulate into conditional probability framework efficient data-driven optimization using generative models. While does not explicitly learn well-established transfer operator or its eigenfunctions, it can effectively encode dynamics leading eigenfunctions system low-dimensional RC space. further quantitatively compare performance with several state-of-the-art algorithms by evaluating quality Markov state models (MSM) constructed their respective spaces, demonstrating superiority three increasingly complex systems. In addition, successfully demonstrated efficacy bias deposition enhanced sampling simple model system. Finally, discuss potential applications downstream such as methods MSM construction.

Язык: Английский

Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding DOI Creative Commons
Jeremy M. G. Leung, Nicolas C. Frazee, Alexander Brace

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture slowest relevant motions. Machine-learning methods can identify in an unsupervised manner have therefore been great interest to simulation community. Here, we developed a general method identifying "on-the-fly" during weighted ensemble (WE) via deep learning (DL) outliers among sampled conformations. Our identifies latent space model system's conformations periodically trained using convolutional variational autoencoder. As proof principle, applied our DL-enhanced WE simulate NTL9 protein folding process. To enable rapid tests, simulations propagated discrete-state synthetic molecular dynamics trajectories generative, fine-grained Markov state model. Results revealed on-the-fly DL enhanced efficiency by >3-fold estimating rate constant. efforts are significant step forward slow rare event sampling.

Язык: Английский

Процитировано

0

Machine Learning and Statistical Mechanics: Shared Synergies for Next Generation of Chemical Theory and Computation DOI
Rose K. Cersonsky,

Bingqing Cheng,

Marco De Vivo

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0

Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics DOI Creative Commons

Ziyue Zou,

Dedi Wang, Pratyush Tiwary

и другие.

Digital Discovery, Год журнала: 2024, Номер 4(1), С. 211 - 221

Опубликована: Ноя. 28, 2024

We present a graph-based differentiable representation learning method from atomic coordinates for enhanced sampling methods to learn both thermodynamic and kinetic properties of system.

Язык: Английский

Процитировано

2

Flow Matching for Optimal Reaction Coordinates of Biomolecular Systems DOI
Mingyuan Zhang, Zhicheng Zhang, Hao Wu

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and decomposability, which we reformulate into conditional probability framework efficient data-driven optimization using generative models. While does not explicitly learn well-established transfer operator or its eigenfunctions, it can effectively encode dynamics leading eigenfunctions system low-dimensional RC space. further quantitatively compare performance with several state-of-the-art algorithms by evaluating quality Markov state models (MSM) constructed their respective spaces, demonstrating superiority three increasingly complex systems. In addition, successfully demonstrated efficacy bias deposition enhanced sampling simple model system. Finally, discuss potential applications downstream such as methods MSM construction.

Язык: Английский

Процитировано

1