Cover Image

Molecular Machine Learning Using Euler Characteristic Transforms

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Toscano-Duran, Victor, Rottach, Florian, Rieck, Bastian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Algebraic Topology Biomolecules
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mathematical Insights into Protein Architecture: Persistent Homology and Machine Learning Applied to the Flagellar Motor
by: Lamine, Zakaria, et al.
Published: (2025)

Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
by: von Rohrscheidt, Julius, et al.
Published: (2024)

Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform
by: Rieck, Bastian
Published: (2024)

Have Graph -- Will Lift? The Case for Higher-Order Benchmarks
by: Rieck, Bastian
Published: (2026)

Mayer-homology learning prediction of protein-ligand binding affinities
by: Feng, Hongsong, et al.
Published: (2024)

New Aspects of Analyzing Amyloid Fibrils
by: Lin, Xiaoxi, et al.
Published: (2024)

Simplicial Representation Learning with Neural $k$-Forms
by: Maggs, Kelly, et al.
Published: (2023)

Mapping the Multiverse of Latent Representations
by: Wayland, Jeremy, et al.
Published: (2024)

Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation
by: Wee, JunJie, et al.
Published: (2024)

Geometry-Aware Simplicial Message Passing
by: Wang, Elena Xinyi, et al.
Published: (2026)

No Triangulation Without Representation: Generalization in Topological Deep Learning
by: Schmidt, Johannes S., et al.
Published: (2026)

Persistent Homology via Ellipsoids
by: Canova, Niklas, et al.
Published: (2024)

Interpretable Classification of Time Series Using Euler Characteristic Surfaces
by: Luwang, Salam Rabindrajit, et al.
Published: (2026)

MANTRA: The Manifold Triangulations Assemblage
by: Ballester, Rubén, et al.
Published: (2024)

On the Expressivity of Persistent Homology in Graph Learning
by: Ballester, Rubén, et al.
Published: (2023)

Euler Characteristic Tools For Topological Data Analysis
by: Hacquard, Olympio, et al.
Published: (2023)

The magnitude vector of images
by: Adamer, Michael F., et al.
Published: (2021)

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
by: Kong, Xiangzhe, et al.
Published: (2023)

The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning
by: Holmgren, Benjamin, et al.
Published: (2024)

Graphcode: Learning from multiparameter persistent homology using graph neural networks
by: Kerber, Michael, et al.
Published: (2024)

Tensor Computation of Euler Characteristic Functions and Transforms
by: Cisewski-Kehe, Jessi, et al.
Published: (2025)

Graph Diffusion Transformers for Multi-Conditional Molecular Generation
by: Liu, Gang, et al.
Published: (2024)

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
by: Kyro, Gregory W., et al.
Published: (2023)

Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
by: Jing, Bowen, et al.
Published: (2023)

Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach
by: Aqeel, Imra, et al.
Published: (2023)

Stability, Complexity and Data-Dependent Worst-Case Generalization Bounds
by: Tuci, Mario, et al.
Published: (2025)

CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability
by: Kyro, Gregory W., et al.
Published: (2024)

Predicting Early and Complete Drug Release from Long-Acting Injectables Using Explainable Machine Learning
by: Robles, Karla N., et al.
Published: (2026)

Generative Molecular Design with Steerable and Granular Synthesizability Control
by: Guo, Jeff, et al.
Published: (2025)

Learning Hierarchical Interaction for Accurate Molecular Property Prediction
by: Hong, Huiyang, et al.
Published: (2025)

Convolutional Persistence Transforms
by: Solomon, Elchanan, et al.
Published: (2022)

SurGBSA: Learning Representations From Molecular Dynamics Simulations
by: Jones, Derek, et al.
Published: (2025)

Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
by: Luo, Yizhen, et al.
Published: (2024)

Deep Manifold Transformation for Protein Representation Learning
by: Hu, Bozhen, et al.
Published: (2024)

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
by: Zhang, Xiangyu
Published: (2024)

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
by: Majewski, Maciej, et al.
Published: (2022)

xPerT: Extended Persistence Transformer
by: Kim, Sehun
Published: (2024)

Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning
by: Suriana, Patricia, et al.
Published: (2025)

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
by: Wang, Danqing, et al.
Published: (2024)

A Model-Centric Review of Deep Learning for Protein Design
by: Kyro, Gregory W., et al.
Published: (2025)