Andrew Reid he/him
Doctoral researcher in Chemistry
Strategic Research Areas
My background is in peptide chemistry and organic synthesis, completing my undergraduate research project on bifunctional disulfide bridge mimetics. It was here that I discovered my interest for practical applications of peptidomimetics.
I have always had a keen interest in drug discovery and medicinal chemistry, so I knew I wanted my project to have an aspect of drug discovery. Peptidomimetics are an emerging class of drug molecules, and I think using machine learning techniques to explore this chemical space will be key in rapid advancements of the field.
In my final year, I won the BSc poster prize for chemistry. Presenting a poster focusing on my bachelor’s research project
Being able to combine techniques used across completely different disciplines unlocks previously untapped possibilities and I think this is a completely underutilised key to scientific advancements. Being able to conduct this research alongside a cohort of supportive, likeminded researchers will be a constant reminder that we are not alone in this journey.
Machine Learning enabled peptidomimetic hit discovery platform
Creating a library of peptidomimetic compounds will allow us to target previously untargetable molecules. They sit in a “goldilocks zone” between small molecules and biologics which allow us to combine the advantages of the respective molecular classes. By creating an extensive library of these compounds, we can cover a large area of chemical space and increase the search area as we focus on hard to target molecules.
Creating such a library will generate large amounts of data that must be processed, analysed, and applied to develop our search. This is where we utilise machine learning techniques such as active learning and neural networks. These will allow us to reduce the level of unnecessary searches and syntheses carried out and will decrease the time taken to go from hit to lead in the discovery process.
I am seeking collaborators who are enthusiastic about advancing medicinal chemistry, computational chemistry, and modern drug discovery. I’m particularly interested in working with researchers who enjoy integrating experimental and computational techniques to solve complex molecular challenges. Ideal collaborators would bring complementary expertise, whether in molecular design, predictive modelling, or taregt selection, to help push the boundaries of what can be achieved with emerging chemical modalities. I value open, interdisciplinary teamwork and hope to connect with like-minded scientists who are excited to explore new ideas, share knowledge, and collectively accelerate meaningful progress in therapeutic discovery.