Enhanced Drug-drug Interaction Prediction Using Adaptive Knowledge Integration

TL;DR

This paper introduces an adaptive knowledge integration framework that enhances drug-drug interaction prediction accuracy by leveraging reinforcement learning to incorporate prior knowledge into large language models, addressing dataset imbalance and complex interactions.

Contribution

It presents a novel reinforcement learning-based method for adaptively infusing prior drug knowledge into LLMs to improve DDIE prediction accuracy and generalization.

Findings

Significant accuracy improvement over baseline models.

Effective handling of imbalanced datasets.

Enhanced generalization to unseen drug combinations.

Abstract

Drug-drug interaction event (DDIE) prediction is crucial for preventing adverse reactions and ensuring optimal therapeutic outcomes. However, existing methods often face challenges with imbalanced datasets, complex interaction mechanisms, and poor generalization to unknown drug combinations. To address these challenges, we propose a knowledge augmentation framework that adaptively infuses prior drug knowledge into a large language model (LLM). This framework utilizes reinforcement learning techniques to facilitate adaptive knowledge extraction and synthesis, thereby efficiently optimizing the strategy space to enhance the accuracy of LLMs for DDIE predictions. As a result of few-shot learning, we achieved a notable improvement compared to the baseline. This approach establishes an effective framework for scientific knowledge learning for DDIE predictions.