  We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN),
for molecule optimization by combining domain knowledge of chemistry and
state-of-the-art reinforcement learning techniques (double $Q$-learning and
randomized value functions). We directly define modifications on molecules,
thereby ensuring 100\% chemical validity. Further, we operate without
pre-training on any dataset to avoid possible bias from the choice of that set.
Inspired by problems faced during medicinal chemistry lead optimization, we
extend our model with multi-objective reinforcement learning, which maximizes
drug-likeness while maintaining similarity to the original molecule. We further
show the path through chemical space to achieve optimization for a molecule to
understand how the model works.
