The current state of cancer therapeutics has been moving away from one-size-fits-all cytotoxic chemotherapy, and towards a more individualized and specific approach involving the targeting of each tumor's genetic vulnerabilities. Different tumors, even of the same type, may be more reliant on certain cellular pathways more than others. With modern advancements in our understanding of cancer genome sequencing, these pathways can be discovered. Investigating each of the millions of possible small molecule inhibitors for each kinase in vitro, however, would be extremely expensive and time consuming. This project focuses on predicting the inhibition activity of small molecules targeting 8 different kinases using multiple deep learning models. We trained fingerprint-based MLPs and simplified molecular-input line-entry specification (SMILES)-based recurrent neural networks (RNNs) and molecular graph convolutional networks (GCNs) to accurately predict inhibitory activity targeting these 8 kinases.
Novel approaches to oncology drug discovery has been largely spurred by the new understanding of how abnormal protein kinase activity has been linked to the development and onset of a variety of diseases. Different tumors, even of the same type, may be more reliant on certain cellular pathways more than others, which can be discovered through cancer genome sequencing. Previous research has been done in order to characterize the structural basis of kinase inhibitor selectivity, and identify potential kinases that are involved in cancer mechanisms, but there is a lacking of research done to predict potential inhibitors. Once kinases have been identified, the IC-50 values, SMILES codes, connectivity, and other features can be utilized in conjunction with deep learning models to predict the activity of small molecule inhibitors @cite_7 .
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Inhibition of kinase activity has received enormous interest as a therapeutic strategy for cancer. This Review discusses the current approaches to develop and characterize new inhibitors.