For example, Schreck et al. trained a policy model to understand the potential costs of candidate reactions computed from reaction templates at a certain synthetic depth, and to select retrosynthetic pathways based on costs. The method was compared with a decomposition heuristic method 2 to prove its ability for synthetic planning. Similarly, in metabolic engineering, Koch et al. presented a code named RetroPath RL, which uses Monte Carlo Tree Search (MCTS) reinforcement learning to rank metabolic reaction rules to enable the development of biosynthetic routes.