Kieran Didi

Generative modelling techniques like diffusion and flow matching have revolutionised fields like image generation and are about to do the same in the scientific domain, particularly in synthetic biology where they are used to generate novel molecules useful as therapeutics, catalysts and many more applications. However, the success rates of these models remain very low, and their controllability remains weak, reducing their practical impact. This project aims to create powerful and practically useful generative models of biomolecules for applications in protein design and beyond. The main question this project intends to answer is What is the most efficient way to model and design a biomolecule computationally so that it has a high chance of fulfilling the design constraints in the real world? Efficiency here involves both computational efficiency in terms of compute resources such as time and memory, but also experimental efficiency in terms of the success rate these designed molecules show once they are manufactured and tested in the laboratory. Themes explored will involve experimetal constraints for design such as fitness measurements, experimental conditions for multi-state design such as density measurements and all-atomistic modelling for finer controllability.