Application
application/molecular-sciences
Molecular, biomolecular, and drug-discovery analysis.
Notes
- Persistent Spectral Graph The founding persistent-Laplacian paper: harmonic spectra recover persistent homology exactly, and non-harmonic spectra carry the geometry it discards. Paper
- Persistent Topological Laplacians A Laplacian family built along a filtration; its zero eigenvalues are persistent homology, and the rest are the geometry persistence throws away. Method
- PETLS A C++ library with Python bindings for building persistent topological Laplacians and computing their matrices and spectra. Package
- prody ProDy, supplying the elastic network models whose training-free normal-mode analysis anchors per-residue flexibility prediction. Environment
- Protein Flexibility Protein motion is measured through incompatible proxies; this guide separates their targets, protocols, and credible comparisons. Application
- TDA and TDL in Molecular Sciences Wee and Jiang's account of how molecular problems pushed topology from description to conditioned, learnable representations. Paper
- TDL for Docking, Screening, and Drug-Target Interaction Suay-García and Falcó's evaluation playbook for topological drug discovery, prescriptive where this corpus's other surveys are descriptive. Paper
- Topological Deep Learning Learn the readout instead of hand-picking it — topological structure as input to a model, or as the domain the model operates on. Method
- Weighted Hodge Laplacians Su, Tong and Wei swap the filtration for a weight function, and finally give the B-factor claim the 346-protein blind evaluation it always needed. Paper