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Deep Learning for Drug Discovery: A Study of Identifying High Efficacy Drug Compounds Using a Cascade Transfer Learning Approach

In this research, we applied deep learning to rank the effectiveness of candidate drug compounds in combating viral cells, in particular, SARS-Cov-2 viral cells. For this purpose, two different datasets from Recursion Pharmaceuticals, a siRNA image dataset (RxRx1), which were used to build and calibrate our model for feature extraction, and a SARS-CoV-2 dataset (RxRx19a) was used to train our model for ranking efficacy of candidate drug compounds. The SARS-CoV-2 dataset contained healthy, uninfected control or “mock” cells, as well as “active viral” cells (cells infected with COVID-19), which were the two cell types used to train our deep learning model. In addition, it contains viral cells treated with different drug compounds, which were the cells not used to train but test our model. We devised a new cascade transfer learning strategy to construct our model. We first trained a deep learning model, the DenseNet, with the siRNA set, a dataset with characteristics similar to the SARS-CoV-2 dataset, for feature extraction. We then added additional layers, including a SoftMax layer as an output layer, and retrained the model with active viral cells and mock cells from the SARS-CoV-2 dataset. In the test phase, the SoftMax layer outputs probability (equivalently, efficacy) scores which allows us to rank candidate compounds, and to study the performance of each candidate compound statistically. With this approach, we identified several compounds with high efficacy scores which are promising for the therapeutic treatment of COVID-19. The compounds showing the most promise were GS-441524 and then Remdesivir, which overlapped with these reported in the literature and with these drugs that are approved by FDA, or going through clinical trials and preclinical trials. This study shows the potential of deep learning in its ability to identify promising compounds to aid rapid responses to future pandemic outbreaks.