Boltzmann Graph Ensemble Embeddings for Aptamer Libraries

• URL: http://arxiv.org/abs/2510.21980v1
• Date: Fri, 24 Oct 2025 19:13:36 GMT
• Title: Boltzmann Graph Ensemble Embeddings for Aptamer Libraries
• Authors: Starlika Bauskar, Jade Jiao, Narayanan Kannan, Alexander Kimm, Justin M. Baker, Matthew J. Tyler, Andrea L. Bertozzi, Anne M. Andrews,
• Abstract summary: Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions.<n>We introduce a thermodynamically parameterized exponential-family random graph (ERGM) embedding that models molecules as Boltzmann-weighted ensembles of interaction graphs.<n>We show that the proposed embedding enables robust community detection and subgraph-level explanations for aptamer affinity, even in the presence of biased observations.
• Score: 37.52407391187203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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