Optimization of Molecules via Deep Reinforcement Learning

Understand

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.