Redwood AI Updates Reactosphere Platform with New Optimization Module
Redwood AI has added an experimental optimization module to its Reactosphere platform, designed to assist R&D teams in chemical process development.
Vancouver-based Redwood AI (CSE: AIRX) has expanded its Reactosphere chemistry platform with a new optimization module. The update integrates Bayesian optimization and structured experimental design to help research teams refine reaction parameters such as reagent concentrations, catalysts, and solvent choices.
By leveraging sequential and batch-sequential workflows, the software suggests subsequent experiments based on prior data, aiming to minimize the trial-and-error cycle typical in chemical development. Additionally, the module includes a sample-size planning feature that estimates the number of trials needed to achieve specific levels of predictive accuracy.
Redwood AI CEO Louis Dron stated that the update is intended to address the iterative nature of chemistry development. The company expects the tool to see application in fields such as pharmaceutical research, material science, and specialized chemical manufacturing where optimizing experimental efficiency is a key operational objective.