Haoting Zhang is about to start the final year of his HDRUK-Turing Wellcome PhD Programme studies at the University of Cambridge. It has already yielded one paper and conference presentation – more are likely to follow. We caught up with him to find out more about his research and his experience of the programme.
Tell us about your PhD project
It is on analysing and predicting the synergies of drug combinations. So it’s mainly in two parts. The first is estimating the synergy of drug combinations where we already have relevant data.
I submitted a paper on this some months ago which has now been published in Bioinformatics and I also made a presentation about it at the ISMB/ECCB 2023 Conference in France. This paper is a foundation for what’s coming afterwards.
The second part of my thesis is about predicting the synergy of unexplored drug combinations, utilising the model I built in the first part and the existing pharmacological, biological and chemical data from combination screenings. In particular, we focus on providing an uncertainty estimation alongside each prediction. This enables decision-making to be informed by the confidence level associated with the prediction.
Is this PhD laying the foundation for something more?
One of the main aims is to provide tools for decision making in prioritising the testing of drug combinations that might be effective for patients.
Say there are a various unexplored possibilities. If we can predict which ones are most likely to work best that should really speed things up. People can work stuff out using a computational model and then test the most promising ones.
It could speed up the drug discovery pipeline, especially in the preclinical phase as it currently takes a lot of time and work and budget to sort through thousands, even tens of thousands of possibilities.
Has the research gone smoothly so far?
Relatively smooth. But at the same time, there are also some slight changes in the direction. That’s to be expected for all research. My supervisor says that if everything is the same as research proposal, then you are probably not challenging the boundaries of science.
I’ve been learning a lot along the process, especially with the industrial collaboration with AstraZeneca. I’ve learned that what’s really important is to understand how can we use our method in real world science and whether it will really be helpful.
What does the future hold after your PhD?
I haven’t really decided yet. But I see myself doing research in the interdisciplinary area of pharma and machine learning. I found that really interesting. I don’t know yet whether I will do a postdoc in a university or go into the industry for some more industrial experience. But I find this interdisciplinary of drug discovery with machine learning really fun. And there are lots of things to be explored.
How are you finding the programme?
It’s really helpful. Getting the leadership training is quite rare in PhD training. We have learned about designing a project and about building medical devices and bringing them to market.
Normally in academic work we won’t think about these. But in this programme we got the opportunity to learn about things as a cohort and to discuss them. It gives great insights into how things work in the real world.