Knowledge Center
Article / Jul 08, 2021
Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization
Source:
Journal of Organic Chemistry, July 8, 2021
We combine random sampling and active machine learning (ML) to optimize the synthesis of isomacroin, executing only 3% of all possible Friedländer reactions. Employing kinetic modeling, we augment machine intuition by extracting mechanistic knowledge and verify that a global optimum was obtained with ML. Our study contributes evidence on the potential of multiscale approaches to expedite the access to chem. matter, further democratizing organic chem. in a data-motivated fashion.
Also in the Knowledge Center
/ May 29, 2025
Mitigating Shear Stress in Spray Drying for RNA‑Loaded Lipid Nanoparticles through Process and Formulation Optimization
Read more
Scientific Article
/ Jun 01, 2025
Improving inhalation delivery of biologics with extra-large particles produced by spray freeze drying
Read more
Scientific Article
/ Jun 20, 2025
Material-sparing method development for liquid-to-solid ratio determination for wet granulation process development
Read more
Scientific Article