Applying deep neural networks to predict pharmacologic properties of drugs and drug repurposing

Deep learning, frequently referred to as artificial intelligence, a branch of machine learning utilizing multiple layers of neurons to model high-level abstractions in data, has outperformed humans in tasks including image, text and voice recognition, autonomous driving and others, and is now being applied to drug discovery and biomarker development. In a study published in Molecular Pharmaceutics, a prestigious journal published by the American Chemical Society, scientists from Insilico Medicine in collaboration with Datalytic Solutions and Mind Research Network trained deep neural networks to predict the therapeutic use of large number of multiple drugs using gene expression data obtained from high-throughput experiments on human cell lines. Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased. When the networks got confused and guessed the therapeutic use of the drugs incorrectly, the drugs often had dual use, indicating the possibility of using DNNs for drug repurposing. This is the first known application of deep learning to drug discovery using transcriptional response data.