2019: Dr Lisa Pilkington, University of Auckland, has been awarded a Rutherford Foundation postdoctoral fellowship for research entitled: ‘Data Science QSAR Strategies and Tools for Medicinal Chemists’
About the Fellow
Lisa says, she was most attracted to her field of research because it combines her two academic passions: chemistry and statistics. She loves the idea that her research could positively impact not only other research that is done in this area but ultimately the lives of others. “Being a researcher is not just a job to me – it is definitely a passion and purpose that I love to pursue every day.”
Lisa is involved in various outreach activities in the wider community, particularly targeting women and girls into science. She has participated in science days at primary schools, teaching school children about science and its place in the world around us. Lisa also contributed to STEM holiday programmes and sharing what life is like being a woman with a science-based career. Lisa contributes to a number of activities to support post-doctoral scientists, including being Chair of the University of Auckland Science Post-Doctoral Society and the post-doctoral fellow representative on the Faculty of Science Research Committee.
Outside of her research, Lisa loves to travel, cook, spend time with family and friends, and discover new experiences.
About the Project
One of the most crucial goals in drug development and design is to establish a quantitative structure-activity relationship (QSAR) to model the relationship between the structure of a compound and its biological activity – the way that it effects living matter. QSARs are important for establishing the best combination of structural features (e.g. functional groups, size, length, etc.) in a compound that provides optimal biological activity, ensuring the drug creates the desired effects. To construct a QSAR, the relationship between the structural features of a set of compounds and their biological activity is investigated and measured. In traditional approaches, construction of a QSAR is difficult and requires a vast number of compounds to be synthesised in a time-consuming and inefficient process.
Dr Lisa Pilkington will be creating a novel QSAR-development framework and a corresponding R statistical software package, utilising data mining and machine learning strategies capable of analysing multiple variables. This approach has the potential to discover important information simultaneously, such as overall trends and variable interrelationships. The benefit of using these data science techniques is an increase in the efficiency of QSAR development and therefore the entire drug discovery process.
The methodology applied by Dr Lisa Pilkington will incorporate features that have not previously been applied to this problem, including custom-designed statistical software for application and use. The framework and R software package created in this project is anticipated to instigate a paradigm shift in the way medicinal chemistry groups conduct their research by providing a definitive and accessible tool for development of custom QSARs.