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.

翻译：生物分子网络是合成生物学新兴技术的基石——从稳健的生物制造与代谢工程，到智能疗法与基于细胞的诊断——同时也为理解自然与生态系统中的复杂动力学提供了一种机制性语言。然而，设计能够实现特定动力学功能的化学反应网络（CRN）在很大程度上仍依赖于人工操作：虽然可以通过模拟来验证一个已提出的网络，但从行为规约出发反向发现一个网络的问题则十分困难，需要大量的人类洞察力来在由非线性且可能具有随机性的动力学所定义的、庞大的拓扑结构与动力学参数空间中探索。本文介绍GenAI-Net，这是一个生成式人工智能框架，它通过将一个提出反应的智能体与基于用户指定目标定义的模拟评估相结合，实现了CRN设计的自动化。GenAI-Net能够在多种设计任务中高效地产生新颖且拓扑结构多样化的解决方案，这些任务包括剂量响应、复杂逻辑门、分类器、振荡器，以及在确定性与随机性场景（包括噪声抑制）下的鲁棒完美适应。通过将规约转化为候选电路家族和可复用的功能模块，GenAI-Net为可编程生物分子电路设计提供了一条通用路径，并加速了从期望功能到可实施机制的转化过程。