[2601.17582] GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design

Quantitative Biology > Quantitative Methods arXiv:2601.17582 (q-bio) [Submitted on 24 Jan 2026 (v1), last revised 27 Feb 2026 (this version, v2)] Title:GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design Authors:Maurice Filo, Nicolò Rossi, Zhou Fang, Mustafa Khammash View a PDF of the paper titled GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design, by Maurice Filo and 3 other authors View PDF Abstract: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 gate...