ARTICULAR

Within ARTICULAR, the team seek to develop novel machine learning approaches, a branch of artificial intelligence research, to learn from past and present manufacturing data and create new knowledge that aids in crucial manufacturing decisions. Machine learning approaches have been successfully applied to inform aspects of drug discovery, upstream of pharmaceutical manufacturing, where large genomic and molecule screening datasets provide rich information sources for analysis and training artificial intelligences. They have also shown promise in classifying and predicting outcomes from individual unit operations used in medicines manufacturing, such as crystallisation.

For the first time, there is an opportunity to use AI approaches to learn from the data and models from across multiple previous development and manufacturing efforts and then address the most commonly encountered problems when manufacturing new pharmaceutical products. Specifically, the processes and operations to employ, the sensors and measurements to deploy to optimally deliver the product and the potential process upsets and their future impact on the quality of the medicine manufactured

Learn more about The Glasgow Arts’ School of Simulation and Visualisation (SimVis) here.