bjing2016/EigenFold

EigenFold is a sophisticated academic project that applied diffusion models to the protein folding problem by leveraging the physics of harmonic oscillators (eigenmodes). While technically clever, its defensibility is low in the current market (Score: 4). Since its release ~3 years ago, the field has been dominated by massive structural biology models from frontier labs and well-funded incumbents, such as AlphaFold 3 (Google DeepMind), ESMFold (Meta), and RFdiffusion (Baker Lab). The project has 179 stars, indicating respectable academic interest but lacking the industrial momentum of tools like OpenFold or the ubiquity of AlphaFold. The 'eigenmode' approach provides a unique multi-resolution lens, but frontier labs have largely moved toward all-atom diffusion or large-scale language-model-based folding which exhibit higher accuracy across broader datasets. The risk of platform domination is high because Google and Meta essentially define the state-of-the-art in this niche, and the displacement horizon is '6 months' as newer models like Boltz-1 and Chai-1 provide superior open-weights alternatives for the same tasks.