Evolutionary chemical learning in dimerization networks

Abstract

We present a novel framework for chemical learning based on Com- petitive Dimerization Networks (CDNs)-- systems in which multiple molecular species, e.g. proteins or DNA/RNA oligomers, reversibly bind to form dimers. We show that these networks can be trained in vitro through directed evolution, enabling the implementation of complex learning tasks such as multiclass classification without digital hardware or explicit parameter tuning. Each molecular species functions analogously to a neuron, with binding affinities acting as tunable synaptic weights. A training protocol involving mutation, selection, and amplification of DNA-based components allows CDNs to robustly discriminate among noisy input patterns. The resulting classifiers exhibit strong output contrast and high mutual information between input and output, especially when guided by a contrastenhancing loss function. Comparative analysis with in silico gradient descent training reveals closely correlated performance. These results establish CDNs as a promising platform for analog physical computation, bridging synthetic biology and machine learning, and advancing the development of adaptive, energy-efficient molecular computing systems. Significance StatementThis study introduces a new paradigm for learning based on chemical reaction networks rather than digital circuits. Using Competitive Dimerization Networks (CDNs)--biomolecular systems in which species reversibly bind to form dimers--complex classification tasks are learned through in vitro directed evolution. This approach eliminates the need for digital hardware or gradient-based optimization, relying instead on intrinsic molecular dynamics for computation. The resulting chemical classifiers achieve high fidelity and robustness to noise, with performance comparable to that of gradient descent training. These findings establish CDNs as a scalable, energy-efficient platform for molecular computing, suggesting broad potential applications in diagnostics, biosensing, synthetic biology, and nanotechnology, where programmable, adaptive chemical systems could serve as alternatives to conventional electronic processors.