Finding new drugs – called 'drug discovery' – is an expensive and time-consuming task.
My colleagues and I – including researchers from the University of Edinburgh and the Spanish National Research Council IBBTEC-CSIC in Santander, Spain – wanted to know if we could train machine learning models to identify new senolytic drug candidates.. The models learned to distinguish between the two, and could be used to predict whether molecules they had never seen before could also be senolytics.
To determine the best-performing model, at the beginning of the process, we separate a small section of the available training data and keep it hidden from the model until after the training process is completed. We determined our best model and set it to make predictions. We gave it 4,340 molecules and five minutes later it delivered a list of results.
We then tested these drug candidates on two types of cells: healthy and senescent. The results showed that out of the 21 compounds, three were able to eliminate senescent cells, while keeping most of the normal cells alive. These new senolytics then underwent further testing to learn more about how they work in the body.
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