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.

Mol-AIR 是一种基于强化学习和自适应内在奖励的分子生成方法，通过结合深度生成模型和增强学习来有效地生成具有特定性质的分子结构，优于现有方法在无先验知识的情况下生成具有期望性质的分子。