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Found 10 papers, 5 papers with code
Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2.
Graph Neural Networks with a Distribution of Parametrized Graphs
Traditionally, graph neural networks have been trained using a single observed graph.
Torsion Graph Neural Networks
This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs.
Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction
The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negativecurvature ratios, network densities, and network sizes (when larger than 500).
Molecular geometric deep learning
This demonstrates the great potential of novel molecular representations beyond the de facto standard of covalent-bond-based molecular graphs.
Persistent Dirac for molecular representation
A series of physical persistent attributes, which characterize the spectrum of the Dirac matrices across a filtration, are proposed and used as efficient molecular fingerprints.
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design
Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as Ollivier persistent Ricci curvature.
Persistent spectral based machine learning (PerSpect ML) for drug design
In this paper, we propose persistent spectral based machine learning (PerSpect ML) models for drug design.
Persistent-Homology-based Machine Learning and its Applications -- A Survey
A suitable feature representation that can both preserve the data intrinsic information and reduce data complexity and dimensionality is key to the performance of machine learning models.
Algebraic Topology
A topological approach for protein classification
However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology.
Biomolecules
