RouteWise – An Integration Friendly Platformfor Interactive Synthesis Routes
Bibliographic record
Abstract
Effective visualization of complex synthesis routes is critical for computeraided synthesis planning (CASP), yet current solutions are limited in scope, integration flexibility, and chemical intuition. We introduce RouteWise, a versatile, containerized web application designed to address these unmet needs. Its modular architecture enables seamless integration into diverse environments, including popular prototyping platforms like Jupyter Notebook/Lab. Essentially, RouteWise adopts a reactioncentric visualization, prioritizing the intuitive representation of synthetic transformations over isolated substances, thereby enhancing usability for chemists. The platform accepts a well-defined, efficient JSON input representing synthesis graphs with routes defined as node cover sets, effortlessly incorporating reaction provenance, metadata (e.g., class, conditions), inventory information, and enabling simultaneous visualization of both evidencebased and predicted routes (e.g., from ASKCOS). Furthermore, RouteWise supports multiple parallel user sessions via a centralized FastAPI server, ensuring data privacy while facilitating automated data transmission from any CASP system. Scientific Contribution: This unique combination of features makes RouteWise the first tool to deliver accessible, chemically intuitive, and robust visualization for the evolving CASP landscape .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".