# GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design

arXiv: https://arxiv.org/abs/2601.17582 · HTML: https://mlsanity.com/paper/2601.17582/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 123 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Theory of Regularized Markov Decision Processes](https://mlsanity.com/paper/1901.11275/) (2019)
- [Algorithms for CVaR Optimization in MDPs](https://mlsanity.com/paper/1406.3339/) (2014)
- [Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets](https://mlsanity.com/paper/2305.17010/) (2023)
- [Discrete and Continuous Action Representation for Practical RL in Video Games](https://mlsanity.com/paper/1912.11077/) (2019)
- [On reaction network implementations of neural networks](https://mlsanity.com/paper/2010.13290/) (2020)
- [Programming and Training Rate-Independent Chemical Reaction Networks](https://mlsanity.com/paper/2109.11422/) (2021)
- [Antithetic Integral Feedback ensures robust perfect adaptation in noisy biomolecular networks](https://mlsanity.com/paper/1410.6064/) (2014)
- [Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation](https://mlsanity.com/paper/2112.10273/) (2021)
- [Automatic Implementation of Neural Networks through Reaction Networks -- Part I: Circuit Design and Convergence Analysis](https://mlsanity.com/paper/2311.18313/) (2023)
- [Minimal motifs for habituating systems](https://mlsanity.com/paper/2407.18204/) (2024)

## Built on by (most cited first, 2 of 2)

- [Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization](https://mlsanity.com/paper/2602.19775/) (2026)
- [Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning](https://mlsanity.com/paper/2605.06552/) (2026)

## Abstract (arXiv)

Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.