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The design and optimization of synthetic metabolic pathways are essential for advancing biomanufacturing efficiency. This research introduces a machine learning-driven framework that integrates Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Random Forests for accurate enzyme efficiency prediction. GNN outperformed other models with 92.4% accuracy. For pathway optimization, Genetic Algorithms and A* Search demonstrated superior speed and convergence compared to reinforcement learning. SHAP analysis provided interpretability by identifying key biochemical features, and Flux Balance Analysis (FBA) validated the biological feasibility of the designed pathways, reducing the need for wet-lab trials by over 50%. GNN-optimized pathways yielded a 36.2% increase in product output. The framework also includes a web-based tool for pathway visualization and analysis. Key contributions include graph-based modeling, hybrid optimization, and explainable AI integration. This approach supports scalable, interpretable, and biologically sound pathway engineering for use in pharmaceuticals, biofuels, and industrial biotechnology.

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Machine Learning-Based Biosynthetic Pathway Design in Synthetic Biology Through Graph Search Transfer Learning and Interpretability

  • 会议论文

  • 发表日期：

    2026年

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  • Premanand Ghadekar,
  • Atharv Joshi,
  • Sujit Wagh,
  • Vaibhav Zendage

摘要

The design and optimization of synthetic metabolic pathways are essential for advancing biomanufacturing efficiency. This research introduces a machine learning-driven framework that integrates Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Random Forests for accurate enzyme efficiency prediction. GNN outperformed other models with 92.4% accuracy. For pathway optimization, Genetic Algorithms and A* Search demonstrated superior speed and convergence compared to reinforcement learning. SHAP analysis provided interpretability by identifying key biochemical features, and Flux Balance Analysis (FBA) validated the biological feasibility of the designed pathways, reducing the need for wet-lab trials by over 50%. GNN-optimized pathways yielded a 36.2% increase in product output. The framework also includes a web-based tool for pathway visualization and analysis. Key contributions include graph-based modeling, hybrid optimization, and explainable AI integration. This approach supports scalable, interpretable, and biologically sound pathway engineering for use in pharmaceuticals, biofuels, and industrial biotechnology.