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PROTDYN: A FOUNDATION PROTEIN LANGUAGE MODEL FOR THERMODYNAMICS AND DYNAMICS GENERATION

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
TransformerProtein Language ModelProtein ensemble generationProtein dynamicsgenerative model
Abstract:

Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike prior approaches that treat these tasks separately, ProTDyn allows flexible i.i.d ensemble sampling and dynamic trajectory simulation. Across diverse protein systems, ProTDyn yields thermodynamically consistent ensembles, faithfully reproduces dynamical properties over multiple timescales, and generalizes to proteins beyond its training data—offering a scalable and efficient alternative to conventional MD simulations.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

ProTDyn presents a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. The paper resides in the 'Foundation Models for Dynamics' leaf under 'Machine Learning-Based Generative Models', which currently contains only one paper in the taxonomy (the original work itself). This indicates a relatively sparse research direction within the broader field of protein conformational ensemble generation, suggesting that large-scale foundation models explicitly targeting both ensemble sampling and temporal dynamics remain uncommon compared to task-specific generative approaches.

The taxonomy tree reveals that ProTDyn's neighboring work primarily falls into sibling categories such as 'Diffusion and Autoencoder Models' (four papers), 'Adversarial and Trajectory-Based Models' (two papers), and 'Internal Coordinate and Specialized Architectures' (two papers). These adjacent leaves focus on specific generative architectures or coordinate representations but do not emphasize the foundation model paradigm or multi-timescale unification. The broader 'Machine Learning-Based Generative Models' branch contains eight papers across four leaves, while the entire taxonomy spans fifty papers across thirty-six topics, positioning ProTDyn in a moderately populated but architecturally distinct subfield.

Among the three contributions analyzed, the literature search examined twenty-four candidates total. The 'ProTDyn foundation protein language model' contribution examined ten candidates and found one potentially refuting prior work, suggesting some overlap with existing foundation or large-scale dynamics models. The 'Multi-timescale training framework' examined ten candidates with no clear refutations, indicating relative novelty in this specific training strategy. The 'Unified three-task framework with temporal positional embeddings' examined four candidates and identified one refutable match, implying that unified task formulations or temporal encoding schemes may have precedent. These statistics reflect a limited search scope and do not constitute exhaustive prior art analysis.

Based on the top-twenty-four semantic matches examined, ProTDyn appears to occupy a sparsely populated niche within protein dynamics modeling, though certain architectural or training components show partial overlap with prior work. The analysis covers a focused subset of the literature and does not capture the full landscape of foundation models in computational biology or recent preprints. The taxonomy structure suggests that while generative models for protein ensembles are well-established, the specific combination of foundation-scale training, multi-timescale dynamics, and unified task formulation remains less explored in the surveyed literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
24
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Protein conformational ensemble and dynamics generation. The field is organized around several complementary strategies for capturing the inherent flexibility of proteins. Machine Learning-Based Generative Models leverage deep learning architectures to learn distributions over conformational space, often bypassing expensive simulations by training on existing trajectory data or experimental structures. Physics-Based and Hybrid Simulation Methods rely on molecular dynamics and enhanced sampling techniques, sometimes augmented with machine learning emulators to accelerate exploration. Integrative Experimental-Computational Approaches combine cryo-EM, NMR, and other experimental data with computational refinement to produce ensembles consistent with observations. AlphaFold-Based Ensemble Prediction exploits the success of structure prediction models to generate diverse conformations, particularly for disordered regions. Additional branches address Specialized Protein Systems, Theoretical Frameworks that underpin ensemble concepts, Computational Tools for workflow automation, Methodological Benchmarking to assess accuracy, and Ligand Binding studies that probe conformational plasticity in functional contexts. Within Machine Learning-Based Generative Models, a particularly active line of work focuses on Foundation Models for Dynamics, which aim to build large-scale, transferable representations of protein motion. PROTDYN[0] exemplifies this direction by developing a foundation model trained on diverse trajectory data to predict dynamics across protein families. This contrasts with earlier generative approaches like Direct Generation Ensembles[1] and Generative MD Trajectories[3], which often targeted specific systems or required extensive simulation input. Meanwhile, methods such as Boltzmann AI Methods[11] and Scalable Emulation[12] emphasize learning physically grounded energy landscapes or accelerating sampling through neural surrogates. PROTDYN[0] sits at the intersection of these themes, seeking to generalize dynamics prediction in a data-driven yet physically informed manner, distinguishing itself from narrower ensemble generators by aspiring to broad applicability and reduced reliance on system-specific parameterization.

Claimed Contributions

ProTDyn foundation protein language model

The authors introduce ProTDyn, a unified generative model that simultaneously performs equilibrium conformational ensemble generation (thermodynamics) and multi-timescale dynamics modeling within a single multi-task architecture, unlike prior approaches that treat these tasks separately.

10 retrieved papers
Can Refute
Multi-timescale training framework

The authors develop a flexible multi-timescale training approach that enables ProTDyn to model protein conformational transitions across diverse timescales from nanoseconds to microseconds, bridging short- and long-timescale dynamics through flexible scheduling.

10 retrieved papers
Unified three-task framework with temporal positional embeddings

The authors design a multi-task architecture that extends ESM3 with temporal positional embeddings to perform three complementary tasks: thermodynamics generation (i.i.d. equilibrium sampling), multiscale dynamics generation (temporally coherent trajectories), and dynamics inpainting (fine-grained trajectory recovery).

4 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ProTDyn foundation protein language model

The authors introduce ProTDyn, a unified generative model that simultaneously performs equilibrium conformational ensemble generation (thermodynamics) and multi-timescale dynamics modeling within a single multi-task architecture, unlike prior approaches that treat these tasks separately.

Contribution

Multi-timescale training framework

The authors develop a flexible multi-timescale training approach that enables ProTDyn to model protein conformational transitions across diverse timescales from nanoseconds to microseconds, bridging short- and long-timescale dynamics through flexible scheduling.

Contribution

Unified three-task framework with temporal positional embeddings

The authors design a multi-task architecture that extends ESM3 with temporal positional embeddings to perform three complementary tasks: thermodynamics generation (i.i.d. equilibrium sampling), multiscale dynamics generation (temporally coherent trajectories), and dynamics inpainting (fine-grained trajectory recovery).

Table of Contents