Template-free techniques have also been developed for CASP systems. Different to the templatebased methods, various ML models were trained to predict single-step reactions, then these models were applied recursively on a target molecule to be synthesized. These ML algorithms included, but not limited to, deep neural networks, Sequence to Sequence (seq2seq), transformer (both borrowed from Natural Language Processing ), Graph Neural Networks (GNN) and Graph-to-Graph (G2G) methods. When approaching planning of multi-step syntheses, it is still critical to develop algorithms to prune reactions or sub-routes and, thus, avoid the excessive computational load; some approaches use Monte Carlo tree search (MCTS), for example. Other challenges facing all ML-based synthesis prediction approaches include: potential model overfitting, locally optimal choices during route search, lack of reaction conditions information, poor quality of available data (bias and incomplete data), and lack of methods to evaluate the predicted results. This explains why in recent years hybrid CASP systems, which incorporate expert-coded rules, ML and well-designed searching heuristics/scores, have become popular. CDI's CASP System