# Related Papers for Controllable Enhancer Generation

This folder collects papers most relevant to the project direction:
predictor-guided enhancer design, controllable regulatory DNA generation,
and discrete diffusion for biological sequence design.

## Downloaded PDFs

| File | Topic | Why it matters for this project |
| --- | --- | --- |
| `papers/deepstarr_nature_genetics_2022.pdf` | DeepSTARR enhancer activity predictor and de novo enhancer design | Use as the core activity-oracle precedent. The paper validates sequence-to-activity prediction and synthetic enhancer design. |
| `papers/expressiongan_regulatory_dna_natcomm_2022.pdf` | Generative design of regulatory DNA with ExpressionGAN | Useful for sequence-quality analysis: naturalness, expression prediction, edit distance, GC composition, and regulatory grammar. |
| `papers/cell_type_directed_synthetic_enhancers_nature_2024.pdf` | Cell-type-directed synthetic enhancer design | Useful for framing enhancer generation as targeted regulatory design and for activity/specificity evaluation. |
| `papers/coda_cell_type_targeting_cre_nature_2024.pdf` | Machine-guided CRE design with cell-type targeting | Useful for on-target/off-target activity analysis and predictor-guided sequence selection. |
| `papers/dna_diffusion_nature_genetics_2026.pdf` | DNA-Diffusion for synthetic regulatory elements | Closest high-impact diffusion reference: activity, specificity, sequence diversity, and experimental validation. |
| `papers/d3_discrete_diffusion_regulatory_activity_2024.pdf` | D3 score-entropy discrete diffusion for regulatory DNA | Closest methodological reference for conditional discrete diffusion and evaluation with functional, sequence, and motif metrics. |
| `papers/drakes_reward_optimized_discrete_diffusion_2024.pdf` | Reward optimization for discrete diffusion | Useful future-work direction: combine diffusion naturalness with predictor/reward optimization. |
| `papers/atgc_gen_controllable_dna_language_models_2025.pdf` | Controllable DNA generation with language models | Useful baseline framing for autoregressive vs masked-recovery objectives and controllability metrics. |
| `papers/rl_regulatory_dna_design_2025.pdf` | Reinforcement learning for CRE design | Useful baseline/future-work reference for optimizing regulatory DNA with biological priors. |

## How to Use These Papers in the Manuscript

Recommended related-work grouping:

1. **Sequence-to-activity models for enhancer design**: DeepSTARR.
2. **Generative design of regulatory DNA**: ExpressionGAN, cell-type-directed synthetic enhancers, CODA.
3. **Controllable genomic language models and reward optimization**: ATGC-Gen, RL regulatory DNA design, DRAKES.
4. **Discrete diffusion for regulatory sequence design**: D3 and DNA-Diffusion.

Recommended positioning for this project:

> This work provides a unified predictor-guided evaluation of autoregressive
> and masked-diffusion genomic foundation models for controllable enhancer
> sequence generation on DeepSTARR.

The strongest paper claim should not be "a completely new model" unless more
training and benchmarking are added. A safer claim is that the work compares
conditioning strategies and evaluation axes for enhancer generation:
activity, specificity, diversity, and biological sequence fidelity.