2025/06/16 by Alexei V. Tkachenko, Tkachenko, Alexei V., Bortolo Matteo Mognetti +3 · 1 citation
Chemistry · Computer Science · #Adaptation and Self-Organizing Systems (nlin.AO) #Computational Drug Discovery Methods #Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Molecular Networks (q-bio.MN) #Molecular spectroscopy and chirality #Statistical Mechanics (cond-mat.stat-mech) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2506.14006
openalex publication_date 2025/06/16 · openalex created_date 2025/10/13 · openalex updated_date 2026/07/28
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.