Retrosynthetic planning#

The retrosynthesis planning algorithm can be adjusted by the configuration file.

Download example configuration#

Quickstart (CLI)#

Run planning using the repository configuration in configs/planning_standard.yaml:

synplan planning \
  --config configs/planning_standard.yaml \
  --targets targets.smi \
  --reaction_rules reaction_rules.tsv \
  --building_blocks building_blocks_stand.smi \
  --policy_network policy_network.ckpt \
  --results_dir planning_results

Configuration file

tree:
  algorithm: uct
  max_iterations: 100
  max_tree_size: 10000
  max_time: 120
  max_depth: 9
  search_strategy: expansion_first
  ucb_type: uct
  c_ucb: 0.1
  backprop_type: muzero
  exclude_small: True
  init_node_value: 0.5
  min_mol_size: 6
  epsilon: 0
  silent: True
  # NMCS-specific parameters (used when algorithm is "nmcs" or "lazy_nmcs")
  nmcs_level: 2
  nmcs_playout_mode: greedy
  lnmcs_ratio: 0.2
node_evaluation:
  evaluation_type: rollout
  evaluation_agg: max
node_expansion:
  top_rules: 50
  rule_prob_threshold: 0.0
  priority_rules_fraction: 0.5

Configuration parameters

Parameter

Description

tree:algorithm

The search algorithm to use. Options are “uct” (Upper Confidence Tree, default), “nmcs” (Nested Monte Carlo Search), “lazy_nmcs” (Lazy NMCS with pruning), “best_first”, “breadth_first”, and “beam”

tree:max_iterations

The maximum number of iterations the tree search algorithm will perform

tree:max_tree_size

The maximum number of nodes that can be created in the search tree

tree:max_time

The maximum time (in seconds) for the tree search execution

tree:max_depth

The maximum depth of the tree, controlling how far the search can go from the root node

tree:ucb_type

The type of Upper Confidence Bound (UCB) used in the tree search. Options include “puct” (predictive UCB), “uct” (standard UCB), and “value” (the initial node value)

tree:backprop_type

The backpropagation method used during the tree search. Options are “muzero” (model-based approach) and “cumulative” (cumulative reward approach)

tree:search_strategy

The strategy for navigating the tree. Options are “expansion_first” (prioritizing the expansion of new nodes) and “evaluation_first” (prioritizing the evaluation of existing nodes)

tree:exclude_small

If True, excludes small molecules from the tree, typically focusing on more complex molecules

tree:min_mol_size

The minimum size of a molecule (the number of heavy atoms) to be considered in the search. Molecules smaller than this threshold are typically considered readily available building blocks

tree:init_node_value

The initial value for newly created nodes in the tree (for expansion_first search strategy)

tree:epsilon

This parameter is used in the epsilon-greedy strategy during the node selection, representing the probability of choosing a random action for exploration. A higher value leads to more exploration

tree:silent

If True, suppresses the progress logging of the tree search

tree:nmcs_level

Nesting level for NMCS and LazyNMCS algorithms. Higher levels provide more thorough search but are more computationally expensive. Defaults to 2

tree:nmcs_playout_mode

Playout mode for NMCS base-level rollouts. Options are “greedy” (best value), “random”, or “policy” (best policy probability). Defaults to “greedy”

tree:lnmcs_ratio

Pruning percentile for LazyNMCS algorithm. Only candidates scoring above this percentile threshold are explored. Value in range [0.0, 1.0]. Defaults to 0.2

node_evaluation:evaluation_agg

The way the evaluation scores are aggregated. Options are “max” (using the maximum score) and “average” (using the average score)

node_evaluation:evaluation_type

The method used for node evaluation. Options include “random” (random number between 0 and 1), “rollout” (using rollout simulations), and “gcn” (graph convolutional networks)

node_expansion:top_rules

The maximum amount of rules to be selected for node expansion from the list of predicted reaction rules

node_expansion:rule_prob_threshold

The reaction rules with predicted probability lower than this parameter will be discarded

node_expansion:priority_rules_fraction

The fraction of priority rules in comparison to the regular rules (only for filtering policy)
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