  In silico drug-target interaction (DTI) prediction is an important and
challenging problem in biomedical research with a huge potential benefit to the
pharmaceutical industry and patients. Most existing methods for DTI prediction
including deep learning models generally have binary endpoints, which could be
an oversimplification of the problem, and those methods are typically unable to
handle cold-target problems, i.e., problems involving target protein that never
appeared in the training set. Towards this, we contrived PADME (Protein And
Drug Molecule interaction prEdiction), a framework based on Deep Neural
Networks, to predict real-valued interaction strength between compounds and
proteins without requiring feature engineering. PADME takes both compound and
protein information as inputs, so it is capable of solving cold-target (and
cold-drug) problems. To our knowledge, we are the first to combine Molecular
Graph Convolution (MGC) for compound featurization with protein descriptors for
DTI prediction. We used multiple cross-validation split schemes and evaluation
metrics to measure the performance of PADME on multiple datasets, including the
ToxCast dataset, and PADME consistently dominates baseline methods. The results
of a case study, which predicts the binding affinity between various compounds
and androgen receptor (AR), suggest PADME's potential in drug development. The
scalability of PADME is another advantage in the age of Big Data.
