Deep Learning for Protein Modeling: From Single Structure to Bound Complex and Thermodynamic Ensemble, With a Focus on Architectural Design
TL;DR
Generative kinetics is indicated as an aspirational frontier in building physically grounded and transferable models, including data availability and fidelity, the choice of inductive biases to pursue generalization, and the need for rigorous model evaluation.
Abstract
We survey modern deep‐learning approaches to protein conformational modeling through the lens of architectural design. We organize the literature into three increasingly expressive paradigms: (I) single‐structure prediction, (II) prediction of molecular binding complexes, and (III) conformational ensemble generation. For each paradigm, we outline a representative set of models to sketch a practical taxonomy, and we summarize their key achievements, limitations, and common evaluation practices. Across the paradigms, we highlight recurring design choices that shape performance and generalization, including enforced SE(3) equivariance versus learned symmetry; MSA‐driven coevolution versus protein language model priors; deterministic prediction versus generative sampling; explicit energetic supervision versus implicit learning; and integrative modeling across heterogeneous data modalities. While single‐structure prediction is now relatively well established, comparable maturity has not yet been reached for binding‐complex prediction and, especially, for generating faithful thermodynamic ensembles with reliable population weights, which remains an open challenge. We discuss open challenges in building physically grounded and transferable models, including data availability and fidelity, the choice of inductive biases to pursue generalization, and the need for rigorous model evaluation. Ultimately, we indicate generative kinetics as an aspirational frontier.