Flow Matching for Optimal Reaction Coordinates of Biomolecular System
- URL: http://arxiv.org/abs/2408.17139v2
- Date: Tue, 24 Dec 2024 05:53:24 GMT
- Title: Flow Matching for Optimal Reaction Coordinates of Biomolecular System
- Authors: Mingyuan Zhang, Zhicheng Zhang, Hao Wu, Yong Wang,
- Abstract summary: Flow matching for reaction coordinates (FMRC) is a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics.
FMRC is based on the mathematical principles of lumpability and decomposability.
- Score: 19.036655133404487
- License:
- Abstract: We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and decomposability, which we reformulate into a conditional probability framework for efficient data-driven optimization using deep generative models. While FMRC does not explicitly learn the well-established transfer operator or its eigenfunctions, it can effectively encode the dynamics of leading eigenfunctions of the system transfer operator into its low-dimensional RC space. We further quantitatively compare its performance with several state-of-the-art algorithms by evaluating the quality of Markov state models (MSM) constructed in their respective RC spaces, demonstrating the superiority of FMRC in three increasingly complex biomolecular systems. In addition, we successfully demonstrated the efficacy of FMRC for bias deposition in the enhanced sampling of a simple model system. Finally, we discuss its potential applications in downstream applications such as enhanced sampling methods and MSM construction.
Related papers
- RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction [42.596399621642234]
Radio map (RM) is a promising technology that can obtain pathloss based on only location.
In this paper, a sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction.
Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio.
arXiv Detail & Related papers (2024-08-16T08:02:00Z) - KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering.
Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions.
Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
arXiv Detail & Related papers (2024-07-18T05:48:24Z) - Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network [2.325005809983534]
This paper develops an efficient Bayesian inference approach for partially observed enzymatic reaction network (SRN)
An interpretable linear noise approximation (LNA) metamodel is proposed to approximate the likelihood of observations.
An efficient posterior sampling approach is developed by utilizing the gradients of the derived likelihood to speed up the convergence of Markov Chain Monte Carlo.
arXiv Detail & Related papers (2024-05-05T01:54:21Z) - Online Variational Sequential Monte Carlo [49.97673761305336]
We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference.
Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
arXiv Detail & Related papers (2023-12-19T21:45:38Z) - Federated Conditional Stochastic Optimization [110.513884892319]
Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML.
This paper proposes algorithms for distributed federated learning.
arXiv Detail & Related papers (2023-10-04T01:47:37Z) - Reaction coordinate flows for model reduction of molecular kinetics [2.6088247674246303]
We introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems.
Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system.
arXiv Detail & Related papers (2023-09-11T23:59:18Z) - Improving and generalizing flow-based generative models with minibatch
optimal transport [90.01613198337833]
We introduce the generalized conditional flow matching (CFM) technique for continuous normalizing flows (CNFs)
CFM features a stable regression objective like that used to train the flow in diffusion models but enjoys the efficient inference of deterministic flow models.
A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference.
arXiv Detail & Related papers (2023-02-01T14:47:17Z) - GANs and Closures: Micro-Macro Consistency in Multiscale Modeling [0.0]
We present an approach that couples physics-based simulations and biasing methods for sampling conditional distributions with Machine Learning-based conditional generative adversarial networks.
We show that this framework can improve multiscale SDE dynamical systems sampling, and even shows promise for systems of increasing complexity.
arXiv Detail & Related papers (2022-08-23T03:45:39Z) - Quaternion Factorization Machines: A Lightweight Solution to Intricate
Feature Interaction Modelling [76.89779231460193]
factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
We propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM) for sparse predictive analytics.
arXiv Detail & Related papers (2021-04-05T00:02:36Z) - Sinkhorn Natural Gradient for Generative Models [125.89871274202439]
We propose a novel Sinkhorn Natural Gradient (SiNG) algorithm which acts as a steepest descent method on the probability space endowed with the Sinkhorn divergence.
We show that the Sinkhorn information matrix (SIM), a key component of SiNG, has an explicit expression and can be evaluated accurately in complexity that scales logarithmically.
In our experiments, we quantitatively compare SiNG with state-of-the-art SGD-type solvers on generative tasks to demonstrate its efficiency and efficacy of our method.
arXiv Detail & Related papers (2020-11-09T02:51:17Z) - Non-convex Learning via Replica Exchange Stochastic Gradient MCMC [25.47669573608621]
We propose an adaptive replica exchange SGMCMC (reSGMCMC) to automatically correct the bias and study the corresponding properties.
Empirically, we test the algorithm through extensive experiments on various setups and obtain the results.
arXiv Detail & Related papers (2020-08-12T15:02:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.