CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation

• URL: http://arxiv.org/abs/2605.02657v1
• Date: Mon, 04 May 2026 14:38:41 GMT
• Title: CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation
• Abstract summary: CARD is a generative framework that employs a novel radix-based decomposition to convert 3D coordinates into mixed discrete-continuous sequences.<n> CARD matches the accuracy of classical computational methods on unseen systems with diverse topologies, while achieving an approximately 40-fold speedup in inference.
• Score: 22.335696881405536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress, existing methods still face key limitations: classical computational approaches remain prohibitively expensive due to their reliance on extensive molecular dynamics simulations, while deep learning-based methods are constrained by either less-expressive generative models or input dimensions tied to a specific system, resulting in negligible generalization. To address these challenges, we propose CARD, a generative framework that employs a novel radix-based decomposition to bijectively convert 3D coordinates into mixed discrete-continuous sequences, enabling coarse-to-fine autoregressive modeling with enhanced expressiveness. Notably, the model corresponds to a distribution with zero free energy, serving as a proposal for absolute free energy computation of arbitrary systems without relying on alchemical pathways. Experiments across diverse tasks demonstrate that CARD matches the accuracy of classical computational methods on unseen systems with diverse topologies, while achieving an approximately 40-fold speedup in inference.

Related papers

• Extending machine learning model for implicit solvation to free energy calculations [37.65679407817425]
  We introduce a graph neural network (GNN)-based implicit solvent model, dubbed Lambda Solvation Neural Network (LSNN)<n>LSNN achieves free energy predictions with accuracy comparable to explicit-solvent alchemical simulations.
  arXiv  Detail & Related papers  (2025-10-23T01:05:44Z)
• TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamics [39.146761527401424]
  We introduce a complementary energy matching term into the loss function.<n>We evaluate our framework on the Chignolin protein using the CGSchNet model.<n>While energy matching did not yield statistically significant improvements in accuracy, it revealed distinct tendencies in how models generalize the free energy surface.
  arXiv  Detail & Related papers  (2025-09-18T04:22:25Z)
• Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models [0.0]
  We propose a latent score-based generative AI framework for learning, non-local closure models and laws in nonlinear dynamical systems.<n>This work addresses a key challenge of modeling complex multiscale dynamical systems without a clear scale separation.
  arXiv  Detail & Related papers  (2025-06-25T19:04:02Z)
• Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison [50.0791489606211]
  We review state-of-the-art nonlinear model order reduction methods.<n>We discuss both general-purpose methods and tailored approaches for (chemical) process systems.
  arXiv  Detail & Related papers  (2025-06-15T11:39:12Z)
• No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
  This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process.<n>Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system.<n>Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
  arXiv  Detail & Related papers  (2025-01-30T18:36:48Z)
• Principled model selection for stochastic dynamics [0.0]
  We show that overfitting arises not just from individual model complexity but also from the growth of possible models.<n>We introduce Parsimonious Inference (PASTIS), a principled method combining likelihood-estimation with extreme value theory to suppress superfluous parameters.
  arXiv  Detail & Related papers  (2025-01-17T18:23:16Z)
• Constrained Synthesis with Projected Diffusion Models [47.56192362295252]
  This paper introduces an approach to generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional process of generative diffusion as a constrained distribution problem to ensure adherence to constraints.
  arXiv  Detail & Related papers  (2024-02-05T22:18:16Z)
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [102.85440102147267]
  We propose a novel generative model named GeoDiff for molecular conformation prediction. We show that GeoDiff is superior or comparable to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-06T09:47:01Z)
• Decimation technique for open quantum systems: a case study with driven-dissipative bosonic chains [62.997667081978825]
  Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics. We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function. We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
  arXiv  Detail & Related papers  (2022-02-15T19:00:09Z)
• Generalized Discrete Truncated Wigner Approximation for Nonadiabtic Quantum-Classical Dynamics [0.0]
  We introduce a linearized semiclassical method, the generalized discrete truncated Wigner approximation (GDTWA) GDTWA samples the electron degrees of freedom in a discrete phase space, and forbids an unphysical growth of electronic state populations. Our results suggest that the method can be very adequate to treat challenging nonadiabatic dynamics problems in chemistry and related fields.
  arXiv  Detail & Related papers  (2021-04-14T21:53:35Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Targeted free energy estimation via learned mappings [66.20146549150475]
  Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences. FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap.
  arXiv  Detail & Related papers  (2020-02-12T11:10:00Z)

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.