Evolutionary chemical learning in dimerization networks

TL;DR

This paper introduces Competitive Dimerization Networks (CDNs), a novel in vitro chemical learning system that uses molecular binding to perform complex classification tasks without digital hardware.

Contribution

It demonstrates how molecular dimerization networks can be trained through directed evolution to implement machine learning tasks, bridging synthetic biology and computational learning.

Findings

CDNs can classify noisy input patterns effectively

Training via mutation, selection, and amplification achieves high performance

Performance closely matches in silico gradient descent methods

Abstract

We present a novel framework for chemical learning based on Competitive 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 contrast-enhancing 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.