# Generating and designing DNA with deep generative models

arXiv: https://arxiv.org/abs/1712.06148 · HTML: https://mlsanity.com/paper/1712.06148/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2017.

## Built on by (most cited first, 30 of 30)

- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [Dirichlet Diffusion Score Model for Biological Sequence Generation](https://mlsanity.com/paper/2305.10699/) (2023)
- [Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design](https://mlsanity.com/paper/2202.03613/) (2022)
- [Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation](https://mlsanity.com/paper/2102.07970/) (2021)
- [Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC](https://mlsanity.com/paper/2212.09925/) (2022)
- [DiscDiff: Latent Diffusion Model for DNA Sequence Generation](https://mlsanity.com/paper/2402.06079/) (2024)
- [Partition-Guided GANs](https://mlsanity.com/paper/2104.00816/) (2021)
- [On Utility and Privacy in Synthetic Genomic Data](https://mlsanity.com/paper/2102.03314/) (2021)
- [Latent Diffusion Model for DNA Sequence Generation](https://mlsanity.com/paper/2310.06150/) (2023)
- [Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate](https://mlsanity.com/paper/2106.09019/) (2021)
- [Fourier Representations for Black-Box Optimization over Categorical Variables](https://mlsanity.com/paper/2202.03712/) (2022)
- [Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets](https://mlsanity.com/paper/2308.10120/) (2023)
- [Improving RNA Secondary Structure Design using Deep Reinforcement Learning](https://mlsanity.com/paper/2111.04504/) (2021)
- [Is novelty predictable?](https://mlsanity.com/paper/2306.00872/) (2023)
- [Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula](https://mlsanity.com/paper/2306.16951/) (2023)
- [From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization](https://mlsanity.com/paper/2401.02019/) (2024)
- [Reinforcement Learning for Sequence Design Leveraging Protein Language Models](https://mlsanity.com/paper/2407.03154/) (2024)
- [Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability](https://mlsanity.com/paper/2410.17005/) (2024)
- [Language Models for Controllable DNA Sequence Design](https://mlsanity.com/paper/2507.19523/) (2025)
- [Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distance](https://mlsanity.com/paper/2510.10706/) (2025)
- [Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem](https://mlsanity.com/paper/2104.03428/) (2021)
- [Differential-Critic GAN: Generating What You Want by a Cue of Preferences](https://mlsanity.com/paper/2107.06700/) (2021)
- [T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy](https://mlsanity.com/paper/2303.02162/) (2023)
- [Description Generation using Variational Auto-Encoders for precursor microRNA](https://mlsanity.com/paper/2311.17970/) (2023)
- [Semantically Rich Local Dataset Generation for Explainable AI in Genomics](https://mlsanity.com/paper/2407.02984/) (2024)
- [Absorb & Escape: Overcoming Single Model Limitations in Generating Genomic Sequences](https://mlsanity.com/paper/2410.21345/) (2024)
- [Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data](https://mlsanity.com/paper/2506.23182/) (2025)
- [Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimation](https://mlsanity.com/paper/2601.00932/) (2026)
- [ReLU Networks for Exact Generation of Similar Graphs](https://mlsanity.com/paper/2604.05929/) (2026)
- [Synthetic data in cryptocurrencies using generative models](https://mlsanity.com/paper/2604.16182/) (2026)

## Abstract (arXiv)

We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using a generative adversarial network; a DNA-based variant of the activation maximization ("deep dream") design method; and a joint procedure which combines these two approaches together. We show that these tools capture important structures of the data and, when applied to designing probes for protein binding microarrays, allow us to generate new sequences whose properties are estimated to be superior to those found in the training data. We believe that these results open the door for applying deep generative models to advance genomics research.