AiZynthFinder Retrosynthesis

Overview

AiZynthFinder performs computer-aided synthesis planning (CASP): a search algorithm — Monte Carlo tree search by default — recursively disconnects a target molecule into precursors, guided by a neural expansion policy that ranks known reaction templates. The search terminates when all precursors are found in a stock (a set of purchasable building blocks) or the maximum depth is reached. Output is a ranked set of reaction trees plus per-target statistics (is_solved, step count, precursors in/out of stock).

Version covered: 4.4.1 (Python 3.10–3.12). The v4 config format differs substantially from v2/v3 as described in the 2020 paper — never copy a config from an old blog post without translating it.

When to Use

• Planning a synthesis route for a designed or purchased target molecule
• Screening a compound library for synthesizability before committing to make-on-demand
• Finding purchasable precursors or building blocks that lead to a scaffold
• Ranking design ideas by route length and by how many precursors fall outside a catalogue
• Enumerating the first retro step only — plausible disconnections without a full tree
• Testing whether a specific bond can be made disconnection-aware (break_bonds) in a route
• Comparing solve rate across two building-block catalogues for the same target set
• Use torchdrug instead when training a retrosynthesis model rather than running route search
• For forward reaction barriers and transition states use neb-irc-activation-energy; for drawing the resulting scheme use rdkit-chemdraw-cdxml