Synthesizing new inorganic functional materials is a practical goal of materials science. While the advances in computational techniques accelerated the virtual design, the actual synthesis of predicted candidate materials still remain as an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the uncertainty of the predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict the inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict the synthetic precursors for the materials synthesized after 2016. The high correlation between the classification score and prediction accuracy suggests that the prediction score can be interpreted as a measure of uncertainty.