Persistent Dirac for molecular representation
Molecular representations are of fundamental importance for the modeling and analysing molecular systems. The successes in drug design and materials discovery have been greatly contributed by molecular representation models. In this paper, we present a computational framework for molecular represent...
Main Authors: | Wee, Junjie, Bianconi, Ginestra, Xia, Kelin |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/171550 |
Similar Items
-
Multi-cover persistence (MCP)-based machine learning for polymer property prediction
by: Zhang, Yipeng, et al.
Published: (2024) -
Persistent-homology-based machine learning: a survey and a comparative study
by: Pun, Chi Seng, et al.
Published: (2022) -
Multiscale persistent functions for biomolecular structure characterization
by: Xia, Kelin, et al.
Published: (2020) -
Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design
by: Liu, Xiang, et al.
Published: (2022) -
Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction
by: Liu, Xiang, et al.
Published: (2022)