\n This notebook focuses on predicting the 3D structure of RNA molecules using sequence and structural data. \n It covers preprocessing, feature engineering, and model optimization to estimate atomic coordinates accurately. \n The workflow emphasizes stability, parameter tuning, and data-driven improvements for reliable 3D predictions.\n
\n\n\n This notebook presents an end-to-end workflow for predicting the 3D structures of RNA molecules. \n We performed comprehensive data preprocessing, implemented a custom dataset, and utilized a fine-tuned RibonanzaNet model \n to generate accurate 3D coordinates for each RNA sequence. The predictions were visualized in 3D plots and compiled into a submission-ready CSV file.\n
\n\n All steps were optimized for stability, reproducibility, and leaderboard performance.\n
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