We have partnered with ASKCOS (askcos.mit.edu), an open‑source computer‑aided synthesis planning platform, to include our building blocks directly in its workflow. ASKCOS, developed and maintained by researchers within the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium (MLPDS, mlpds.mit.edu), combines multiple retrosynthesis models, multi‑step tree search, reaction condition recommendation, and outcome prediction within a single API‑accessible framework.
A curated subset of 176,564 Chemspace building blocks is now indexed in the ASKCOS buyables database. Proposed synthetic routes can terminate in directly searchable Chemspace items, creating a true one‑stop shop for pathway prediction and chemical ordering.
With Chemspace data inside ASKCOS, users can:
- Design synthetic routes using state‑of‑the‑art retrosynthetic planning
- See real‑world availability, pricing, and lead times for Chemspace building blocks
- Seamlessly move from computational route planning to ordering required chemicals via the Chemspace platform
This integration streamlines the journey from idea to experiment, helping chemists/researchers go from predicted pathways to ready‑to‑order materials in a single environment.
