  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 superior performance over
existing approaches in generating molecules with desired properties without any
prior knowledge, including penalized LogP, QED, and celecoxib similarity. We
believe that Mol-AIR represents a significant advancement in drug discovery,
offering a more efficient path to discovering novel therapeutics.
