  Drug targets are the main focus of drug discovery due to their key role in
disease pathogenesis. Computational approaches are widely applied to drug
development because of the increasing availability of biological molecular
datasets. Popular generative approaches can create new drug molecules by
learning the given molecule distributions. However, these approaches are mostly
not for target-specific drug discovery. We developed an energy-based
probabilistic model for computational target-specific drug discovery. Results
show that our proposed TagMol can generate molecules with similar binding
affinity scores as real molecules. GAT-based models showed faster and better
learning relative to GCN baseline models.
