Scientists use machinelearning to fast-track drug formulation development NatureComms
"This study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables," said Christine Allen, professor in pharmaceutical sciences at the Leslie Dan Faculty of Pharmacy, University of Toronto."We've seen how machine learning has enabled incredible leap-step advances in the discovery of new molecules that have the potential to become medicines.
"AI is transforming the way we do science. It helps accelerate discovery and optimization. This is a perfect example of a 'Before AI' and an 'After AI' moment and shows how drug delivery can be impacted by this multidisciplinary research," said Alán Aspuru-Guzik, professor in chemistry and computer science, University of Toronto who also holds the CIFAR Artificial Intelligence Research Chair at the Vector Institute in Toronto.
"Once we had the data set, we split it into two subsets: one used for training the models and one for testing. We then asked the models to predict the results of the test set and directly compared with previous experimental data. We found that the tree-based models, and specifically lightGBM, delivered the most accurate predictions," said Pauric Bannigan, research associate with the Allen research group at the Leslie Dan Faculty of Pharmacy, University of Toronto.
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