# Boltzmann Graph Ensemble Embeddings for Aptamer Libraries

arXiv: https://arxiv.org/abs/2510.21980 · HTML: https://mlsanity.com/paper/2510.21980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [ncRNA Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1905.06515/) (2019)
- [Hierarchy and assortativity as new tools for affinity investigation: the case of the TBA aptamer-ligand complex](https://mlsanity.com/paper/1706.08324/) (2017)

## Built on by (most cited first, 1 of 1)

- [EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic Peptides](https://mlsanity.com/paper/2607.21561/) (2026)

## Abstract (arXiv)

Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate on a single graph, typically the minimal free energy (MFE) structure, for low-energy ensembles (conformations) representative of structures at thermodynamic equilibrium. We introduce a thermodynamically parameterized exponential-family random graph (ERGM) embedding that models molecules as Boltzmann-weighted ensembles of interaction graphs. We evaluate this embedding on SELEX datasets, where experimental biases (e.g., PCR amplification or sequencing noise) can obscure true aptamer-ligand affinity, producing anomalous candidates whose observed abundance diverges from their actual binding strength. We show that the proposed embedding enables robust community detection and subgraph-level explanations for aptamer ligand affinity, even in the presence of biased observations. This approach may be used to identify low-abundance aptamer candidates for further experimental evaluation.