Srijit Seal, a PhD student in Chemistry at Clare Hall, University of Cambridge, has led a team to build a machine learning model that takes measurements from human cells, making predictions about drug safety.
The discovery of new drugs can be a complex process, so any tools that help to accelerate the process are critical. According to this study by the Bender Group, published in Communications Biology, a machine learning model developed uses Cell Painting* and gene expression data to make predictions of mitochondrial toxicity 60% better than using compound structures only.
On publication of this study, Srijit comments:
Pharmaceutical companies and academics can use this model to determine whether you have mitochondrial toxicity in a compound, which is a leading cause of late-stage drug withdrawals. This saves time and resources by identifying good, non-mitotoxic candidates before scientists start clinical tests.
*Cell Painting is when a cell is flooded with different dyes that stick to different features of the cell. This colourful cell is then profiled with a lot of detail and the results of this profiling are stored in a digital library. It is useful for general investigations and spotting patterns in large amounts of data. The model developed by the Bender Group spotted patterns in these data – for example, certain types of granular in the cell painting data indicated mitochondrial toxicity.
This model is now publicly accessible to researchers who aim to predict mitochondrial toxicity of compounds.
Srijit’s PhD research is supported by a Cambridge International Scholarship from the Cambridge Trust, the Jawaharlal Nehru Memorial Fund, a Trinity Henry Barlow Scholarship from Trinity College, and his MPhil research was supported by the Clare Hall India Innovation Master’s Studentship.
He recently won a Student Poster Award from the Society for Laboratory Automation and Screening (SLAS), and was Clare Hall Graduate Student Body Committee (GSB) President 2021/22.
Story queries: email@example.com