Predictive Scalability in Developing Large Molecule Therapeutics

The emerging tools of 'Digital Molecular Technology' promise a step-change transformation of molecule development processes, mainly through new capabilities in data generation, data analysis and knowledge generation. The Lapkin group has developed significant expertise in machine learning (ML) methods for data analysis in formulations and chemical process development. This project will use a combination of mechanistic and non-linear multi-variate statistical models for decision support and knowledge identification in the synthesis of large molecule therapeutics. The key challenge will be the development of a generic workflow that will be flexible in the use of ML tools for the identification of variable interdependencies and sensitivities. The project will start from exploring the possibility of using past experimental data to generate initial grey models, and to test the hypothesis of using past generalised knowledge, then moving to developing decision support tools.