ArticleScientific reports2023
Persistent Dirac for molecular representation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Persistent Stanley-Reisner Theory.Foundations of data science (Springfield, Mo.) · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data.Advanced intelligent discovery · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- PERSISTENT DIRECTED FLAG LAPLACIAN.Foundations of data science (Springfield, Mo.) · 2025Article
- PERSISTENT INTERACTION TOPOLOGY IN DATA ANALYSIS.Foundations of data science (Springfield, Mo.) · 2025Article
- PERSISTENT SHEAF LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2025Article
- Khovanov Laplacian and Khovanov Dirac for knots and links.Journal of physics. Complexity · 2025Article
- Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
- Persistent Topological Laplacians-A Survey.Mathematics (Basel, Switzerland) · 2025Article
- Persistent Mayer Dirac.Journal of physics. Complexity · 2024Article
- PERSISTENT DIRAC OF PATHS ON DIGRAPHS AND HYPERGRAPHS.Foundations of data science (Springfield, Mo.) · 2024Article
- CHATGPT FOR COMPUTATIONAL TOPOLOGY.Foundations of data science (Springfield, Mo.) · 2024Article
- PERSISTENT HYPERDIGRAPH HOMOLOGY AND PERSISTENT HYPERDIGRAPH LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
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 representation that is mathematically rigorous and based on the persistent Dirac operator. The properties of the discrete weighted and unweighted Dirac matrix are systematically discussed, and the biological meanings of both homological and non-homological eigenvectors are studied. We also evaluate the impact of various weighting schemes on the weighted Dirac matrix. Additionally, a set of physical persistent attributes that characterize the persistence and variation of spectrum properties of Dirac matrices during a filtration process is proposed to be molecular fingerprints. Our persistent attributes are used to classify molecular configurations of nine different types of organic-inorganic halide perovskites. The combination of persistent attributes with gradient boosting tree model has achieved great success in molecular solvation free energy prediction. The results show that our model is effective in characterizing the molecular structures, demonstrating the power of our molecular representation and featurization approach.
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