发表机构
Dayhoff Labs, Inc.; Complex Systems Lab, Universitat Pompeu Fabra（戴霍夫实验室有限公司; 庞培法布拉大学复杂系统实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过逆向设计热力学一致的化学反应网络，利用隐式微分训练自由能景观，发现非平衡驱动力是增强化学计算表现力的最有效单一资源。
AI 中文摘要
生命系统通过将环境信号映射到特定的内部化学状态来进行化学计算。尽管分子编程近期取得了进展，但哪些物理化学特征控制化学系统的计算表现力仍不清楚。在此，我们逆向设计了热力学一致的化学反应网络，其对环境输入的稳态响应计算目标非线性函数。利用隐式微分，我们直接训练自由能景观：标准化学势、过渡态能量和热力学驱动力。由基本连接和切割步骤生成的日益增大的网络，能拟合日益复杂的非单调多项式函数，其表现力随网络大小呈对数增长，主要由反应数量预测。训练单个能量参数类别揭示，能够打破细致平衡的内部热力学驱动力主导可训练性，仅成对的参数类别可实现相当的性能。这些结果确定非平衡驱动力是化学反应网络中稳态计算表现力最有效的单一资源。
英文摘要
Living systems compute with chemistry by mapping environmental signals onto specific internal chemical states. Despite recent advances in molecular programming, it remains unclear which physicochemical features control the computational expressivity of chemical systems. Here we inverse-design thermodynamically consistent chemical reaction networks whose steady-state response to an environmental input computes a target nonlinear function. Using implicit differentiation we train the free-energy landscape directly: standard chemical potentials, transition-state energies and thermodynamic drives. Increasingly large networks generated by elementary ligation and cleavage steps fit increasingly complex nonmonotonic polynomial functions, with expressivity scaling logarithmically with network size, predicted primarily by the number of reactions. Training individual energetic parameter classes reveals that internal thermodynamic drives, capable of breaking detailed balance, dominate trainability, with comparable performances achieved only by pairs of parameter classes. These results identify nonequilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.
