retrosynthesis planning
**Retrosynthesis Planning** in chemistry AI refers to the application of machine learning and search algorithms to automatically design synthetic routes for target molecules by recursively decomposing them into simpler, commercially available precursors through known or predicted chemical reactions. AI retrosynthesis automates the creative process that traditionally requires expert organic chemists, enabling rapid route design for novel molecules.
**Why Retrosynthesis Planning Matters in AI/ML:**
Retrosynthesis planning is **transforming synthetic chemistry** from an expert-dependent art into a systematic, AI-driven science, enabling rapid synthetic route design for the millions of novel molecules proposed by generative drug discovery and materials design programs.
• **Template-based methods** — Reaction templates (SMARTS patterns) extracted from reaction databases are applied in reverse to decompose target molecules; models like Neuralsym and LocalRetro use neural networks to rank applicable templates, selecting the most likely retrosynthetic disconnections
• **Template-free methods** — Sequence-to-sequence models (Molecular Transformer, Chemformer) directly predict reactant SMILES from product SMILES without predefined templates, treating retrosynthesis as a machine translation problem; these can propose novel disconnections not in training data
• **Search algorithms** — Multi-step retrosynthesis uses tree search (Monte Carlo Tree Search, A*, beam search, proof-number search) to explore the space of possible synthetic routes, evaluating partial routes using learned heuristics and terminating when all leaves are commercially available
• **ASKCOS platform** — The open-source Automated System for Knowledge-based Continuous Organic Synthesis integrates retrosynthesis prediction, forward reaction prediction, condition recommendation, and buyability checking into an end-to-end route planning system
• **Evaluation metrics** — Routes are evaluated on: number of steps (shorter = better), starting material cost and availability, reaction yield predictions, route diversity, and expert chemist assessment of practical feasibility
| Method | Approach | Novel Rxns | Multi-Step | Accuracy (Top-1) |
|--------|----------|-----------|-----------|------------------|
| Neuralsym | Template ranking (NN) | No | With search | 45-55% |
| LocalRetro | Local template + GNN | Limited | With search | 50-55% |
| Molecular Transformer | Seq2seq (template-free) | Yes | With search | 45-55% |
| Chemformer | Pretrained seq2seq | Yes | With search | 50-55% |
| Graph2Edits | Graph edit prediction | Yes | With search | 48-52% |
| MEGAN | Graph-based edits | Yes | With search | 49-53% |
**Retrosynthesis planning AI democratizes synthetic chemistry expertise by automating the creative decomposition of target molecules into feasible synthetic routes, combining learned chemical knowledge with systematic search to design practical synthesis pathways for novel drug candidates and functional materials at a pace that far exceeds human expert capacity.**