  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.
