Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation

Optimizing techniques
for discovering molecular structures with
desired properties is crucial in artificial intelligence (AI)-based
drug discovery. Combining deep generative models with reinforcement
learning has emerged as an effective strategy for generating molecules
with specific properties. Despite its potential, this approach is
ineffective in exploring the vast chemical space and optimizing particular
chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using
adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based
and learning-based intrinsic rewards by exploiting random distillation
network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing
approaches in generating molecules having the desired properties,
including penalized LogP, QED, and celecoxib similarity, without any
prior knowledge. We believe that Mol-AIR represents
a significant advancement in drug discovery, offering a more efficient
path to discovering novel therapeutics.