Optimization of Molecules via Deep Reinforcement Learning

TL;DR

This paper introduces MolDQN, a reinforcement learning framework that optimizes molecules by ensuring chemical validity and balancing multiple objectives without pre-training, advancing drug discovery processes.

Contribution

The paper develops MolDQN, a novel RL-based method for molecule optimization that guarantees validity, avoids dataset bias, and incorporates multi-objective optimization in medicinal chemistry.

Findings

Successfully optimizes molecules for drug-likeness and similarity.

Ensures 100% chemical validity during modifications.

Provides insights into chemical space navigation.

Abstract

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.