Implementation of Support Vector Machines using Reaction Networks
Amey Choudhary, Jiaxin Jin, Abhishek Deshpande
TL;DR
This work demonstrates that a chemical reaction network (CRN) can implement a hard-margin support vector machine (SVM) by constructing modular reaction-building blocks for dual-rail encoding, arithmetic, comparison, and sequential execution. The feedforward and learning components are encoded as mass-action dynamics and simulated with ODEs, enabling loading, inference, and gradient-descent updates within a biochemical setting. Experimental results on a synthetic 2D dataset show that CRN-derived weights and bias converge close to those obtained by conventional SVM implementations, with small average errors and coherent hyperplane trajectories. The proposed framework extends molecular computation toward practical machine-learning tasks and highlights avenues for scaling to soft-margin, kernel-based, and larger-scale learning in synthetic biology and chemical computing contexts.
Abstract
Can machine learning algorithms be implemented using chemical reaction networks? We demonstrate that this is possible in the case of support vector machines (SVMs). SVMs are powerful tools for data classification, leveraging VC theory to handle high-dimensional data and small datasets effectively. In this work, we propose a reaction network scheme for implementing SVMs, utilizing the steady-state behavior of reaction network dynamics to model key computational aspects of SVMs. This approach introduces a novel biochemical framework for implementing machine learning algorithms in non-traditional computational environments.