More than three years after CORENET researchers set out to implement reservoir computing based on reaction networks as an enabling technology to accelerate discovery in the chemical sciences, eight scientific articles have been successfully published, including studies from partners at the Autonomous University of Madrid (UAM), Radboud Universiteit, IBM, Syddansk Universitet (SDU) and Universität Bielefeld.

Nogal et al. (2023) articulates how protometabolic chemical reaction networks (CRNs) naturally form self-sustaining cycles and catalytic interconnections, framing them as primitive information‑processing systems whose modular scalability mirrors principles behind reservoir computing. The following study by Nogal et al. (2024) on NADH‑mediated abiotic amino‑acid synthesis then demonstrates robust chemical networks capable of multistep reactions driven by a single cofactor, showing emergent computation‑like behavior in molecular networks. The editorial by Gentili et al. (2024) and the perspective by Csizi & Lörtscher (2024) in Frontiers in Neuroscience underline the potential of complex CRNs as low‑energy analog neuromorphic platforms, directly highlighting their relevance for brain‑inspired computing architectures based on molecular reactions. The landmark demonstration of chemical reservoir computation in a self‑organizing reaction network by Baltussen et al. (2024) provides empirical proof that complex networks (e.g. formose reaction mixtures) can process information and perform classification tasks in a reservoir‑computing framework at molecular scale. Meanwhile, the study by Vela‑Gallego et al. (2023) on self‑assembling lipopeptides functionalized with nucleobases and peptides shows chemically programmable assemblies whose catalytic activity, and thus network reactivity and information flow, can be tuned by molecular design. The reinforcement‑learning approach to estimating reaction barriers by Pal (2024) suggests computational frameworks that can be applied to optimize dynamic CRNs for computing tasks, enabling feedback‑guided tuning of network performance. Finally, the study by Pal et al. (2025) introduces a modeling approach based on mixed integer linear programming (MILP) that integrates thermodynamic constraints into CRN pathway search, ensuring only energetically favorable reactions are selected and enabling ranking of alternative pathways.

Together, these eight studies collectively demonstrate the theoretical foundations, empirical validations, programmable molecular hardware, and computational tools critical to advancing scalable reservoir computing based on complex CRNs as transformative technology for accelerating chemical discovery and enabling brain-inspired molecular information processing devices.