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

TL;DR

Mol-AIR is a reinforcement learning framework that uses adaptive intrinsic rewards to improve goal-directed molecular generation, significantly enhancing efficiency in discovering molecules with desired properties in drug discovery.

Contribution

Introduces Mol-AIR, a novel RL-based method combining history-based and learning-based intrinsic rewards for effective molecular generation without prior knowledge.

Findings

Outperforms existing methods in benchmark tests

Effectively generates molecules with desired properties

Does not require prior knowledge for optimization

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 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.