# VANDAM: Viewing a nucleotide sequence with DNA molecular priors

arXiv: https://arxiv.org/abs/2610.00411 · HTML: https://mlsanity.com/paper/2610.00411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling](https://mlsanity.com/paper/2403.03234/) (2024)
- [A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions](https://mlsanity.com/paper/2310.03281/) (2023)
- [Genomic Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2407.11435/) (2024)
- [JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures](https://mlsanity.com/paper/2602.17162/) (2026)
- [D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation](https://mlsanity.com/paper/2603.01780/) (2026)
- [A Phylogenetic Approach to Genomic Language Modeling](https://mlsanity.com/paper/2503.03773/) (2025)

## Abstract (arXiv)

Contemporary Genomic Foundation Models (GFMs) rely on a DNA-as-a-string paradigm that employs masked token prediction objectives for pretraining. However, this abstraction does not explicitly model the biochemical, structural, and physical properties essential to biological function. Many molecular properties can be estimated from sequence using established biophysical models, so their utility lies not in providing an independent modality, but in introducing priors that training objectives can explicitly exploit. We introduce VANDAM, a framework that extends the training of GFMs with DNA molecular priors. In self-supervised training, VANDAM predicts regional molecular properties from pooled representations. When functional labels are available and can reward retaining molecular priors, local features are additionally injected at the input. VANDAM consistently improves downstream performance across four architecture families and nine held-out genomic tasks by complementing token-based objectives. Probing experiments further demonstrate that the use of molecular priors generalizes to other unseen molecular properties.