We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| schwallergroup/steer | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| format_path | Ran | schwallergroup/steer/src/steer/mechanism/astar.py code served (permissive licence) · get_code("1437a283b1c4763f") |
| opposite_score | Ran | schwallergroup/steer/src/steer/mechanism/search_algo.py code served (permissive licence) · get_code("4f2bf2d5e80a790f") |
| setup_logger | Ran | schwallergroup/steer/src/steer/logger.py code served (permissive licence) · get_code("1f00017309f3b210") |
| softmax | Ran | schwallergroup/steer/src/steer/mechanism/search.py code served (permissive licence) · get_code("c0cf1196424d4f71") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
While automated chemical tools excel at specific tasks, they have struggled to capture the strategic thinking that characterizes expert chemical reasoning. Here we demonstrate that large language models (LLMs) can serve as powerful tools enabling chemical analysis. When integrated with traditional search algorithms, they enable a new approach to computer-aided synthesis that mirrors human expert thinking. Rather than using LLMs to directly manipulate chemical structures, we leverage their ability to evaluate chemical strategies and guide search algorithms toward chemically meaningful solutions. We demonstrate this paradigm through two fundamental challenges: strategy-aware retrosynthetic planning and mechanism elucidation. In retrosynthetic planning, our system allows chemists to specify desired synthetic strategies in natural language -- from protecting group strategies to global feasibility assessment -- and uses traditional or LLM-guided Monte Carlo Tree Search to find routes that satisfy these constraints. In mechanism elucidation, LLMs guide the search for plausible reaction mechanisms by combining chemical principles with systematic exploration. This approach shows strong performance across diverse chemical tasks, with newer and larger models demonstrating increasingly sophisticated chemical reasoning. Our approach establishes a new paradigm for computer-aided chemistry that combines the strategic understanding of LLMs with the precision of traditional chemical tools, opening possibilities for more intuitive and powerful chemical automation systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.08537")
get_code_for_paper("2503.08537")
have("2503.08537")
Connect an agent — have() is free.