Entellect Reaction Workbench: Better predictive outcomes from solid data foundations in reaction chemistry
Strengthen your innovation power with insights generated from integrated, harmonized data—both proprietary and external. With Entellect's Reaction Workbench chemists can benefit from better predictive outcomes from solid data foundations and data scientists can spend more time on high value work and less time cleaning and normalizing data.
Entellect’s Reaction Workbench will improve predictive reaction chemistry outcomes—whether in drug discovery or new chemicals research.
Want to see for yourself?
Stay informed about Entellect’s Reaction Workbench.
Who is it for?
Entellect’s Reaction Workbench has benefits that span across multiple areas across an organization. Reaction prediction based on solid data foundations will, ultimately, result in quicker insights that will lead to better innovation and business outcomes.
Data scientists/computational chemists:
- Reduce the need for labor-intensive data ingestion and harmonization, so you can spend more time on high-value predictive algorithms
- Streamlined deployment process ensures that your work is not restricted to a small group of people
- Easily update models based on new incoming data
- Assess results from different approaches (e.g., algorithm-generated results and rule-based approaches) and combine the findings to generate better outcomes
Heads of Knowledge Management:
- Ensures that discovery chemistry output is based on solid data foundations
- A vast array of disparate data sets to derive value
- Feel confident that the output and findings are based on FAIR Data
Making data more FAIR
How can we bridge information silos so that data becomes findable, accessible, interoperable and reusable? A FAIR Server could be the answer.
How it’s developed
Entellect’s Reaction Workbench leverages Entellect’s unique data linking and contextualizing engine. It is based on a modern architecture and strong data governance principles and is FAIR from the ground up.
The Reaction Workbench follows a clear workflow, of which the starting point is the clean data foundation. The modelling factory environment provides data scientists with familiar tools to build or train models for predictive reaction chemistry, and provides the requisite amount of CPU/GPU and storage. And, finally, the deployment is made simple via a one-click API enabled mechanism that deploys the output directly to the bench chemist’s application of choice—for example, their ELN for effective reaction prediction.
Why choose Entellect’s Reaction Workbench
Entellect’s Reaction Workbench supports predictive reaction capabilities. It is a space where data scientists and computational chemists can access combined, normalized, clean, internal and external reaction data. From this they can create their own algorithms, or train existing ones, for effective predictive reaction analytics.
One-click deployment back to the bench chemists will significantly increase the utility and reach of the algorithms, transforming not only how the data scientists work, but how the bench chemists consume that output.
Data scientists using the Reaction Workbench will benefit from:
- Integrated proprietary reaction data and/or other third-party data alongside Reaxys reaction and USPTO data
- Not having to harmonize and cleanse the data before performing the data science
- One-click deployment of models built on solid data foundations to the bench chemists
- Models deployed seamlessly into end-user applications of choice for better end-point outcomes
Entellect’s Reaction Workbench can be applied in many areas of predictive reaction chemistry:
- Leverage internal failed reaction data to identify patterns for more effective routes for complex molecules
- Create or evaluate atom mapping algorithms
- Construct reaction classifications for more effective similarity searches or reaction clustering
- Predict metabolites, or analyze metabolite networks of a given organism
- Retrosynthesis: estimate chemical accessibility for library design