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

arXiv:2403.20109v1 Announce Type: new
Abstract: 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 …

abstract artificial artificial intelligence arxiv cs.ai cs.lg deep generative models discovery drug discovery generative generative models intelligence intrinsic molecules q-bio.bm reinforcement reinforcement learning strategy type