Interpretable Machine Learning Framework for Predicting Spider Silk Toughness and Tensile Strength From Physicochemical and Genetic Features

ABSTRACT Spider silk has exceptional mechanical properties, notably its high toughness, which is a measure of a material's ability to absorb energy before failure. Predicting toughness using physical, biochemical, and genetic features is a challenging task due to the nonlinear and multivariate interactions involved. This study presents a comprehensive machine learning framework to predict the toughness and the tensile strength of spider silk fibers using interpretable and high‐performing models. A curated dataset with varied physicochemical and structural features was used to train, tune, and evaluate multiple machine learning models, including Decision Tree, support vector machines, Random Forest, Gradient Boosting, and XGBoost. Feature engineering steps introduced domain‐specific constructs such as a toughness proxy and modulus transformations. Hyperparameter tuning was conducted via Bayesian optimization to enhance model performance. Among all tested models, the tuned XGBoost regressor achieved the highest predictive accuracy ( and 0.765), outperforming all other models. Feature importance analysis highlighted several key predictors among Young's modulus, aligning with known biological mechanisms for both toughness and tensile strength. This work demonstrates how machine learning can be used not only for accurate prediction but also to uncover the underlying determinants of silk toughness. The proposed framework sets the stage for data‐driven design of bioinspired synthetic fibers and represents a significant step toward the computational modeling of high‐performance biomaterials.