  Machine learning and deep learning have gained popularity and achieved
immense success in Drug discovery in recent decades. Historically, machine
learning and deep learning models were trained on either structural data or
chemical properties by separated model. In this study, we proposed an
architecture training simultaneously both type of data in order to improve the
overall performance. Given the molecular structure in the form of SMILES
notation and their label, we generated the SMILES-based feature matrix and
molecular descriptors. These data were trained on a deep learning model which
was also integrated with the Attention mechanism to facilitate training and
interpreting. Experiments showed that our model could raise the performance of
prediction comparing to the reference. With the maximum MCC 0.58 and AUC 90% by
cross-validation on EGFR inhibitors dataset, our architecture was outperforming
the referring model. We also successfully integrated Attention mechanism into
our model, which helped to interpret the contribution of chemical structures on
bioactivity.
