LOGIC: LLM-Driven Cross-Scale Feature Coupling for Drug-Disease Interaction Prediction.

Abstract

Predicting drug-disease interactions (DSI) is a pivotal task in computational drug discovery, aiming to identify potential therapeutic or adverse effects between drugs and diseases. Current methodologies primarily model on two levels of features: the macroscopic level, utilizing network topology, and the mesoscopic level, leveraging molecular-level features. While valuable, these approaches share a common shortcoming: they frequently fail to capture fine-grained, mechanistic interactions. This refers to the specific interplay between drug functional groups and disease symptoms that underpins pharmacological effects. To address this limitation, we propose LOGIC, a novel model for LLM-driven cross-scale feature coupling for DSI prediction. LOGIC comprehensively models drug and disease representations across micro-, meso-, and macro-scales. The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional domain knowledge. LOGIC mainly consists of four modules: (1) Drug-disease micro-scale feature learning; (2) Drug-disease meso-scale feature learning; (3) Drug-disease macro-scale feature learning; and (4) cross-scale feature coupling prediction, which integrates micro-, meso- and macro-scale features for both drugs and diseases, and employs the matrix multiplication operation to model fine-grained feature interactions in the dimension level for DSI prediction. Extensive experiments conducted on multiple datasets validate the effectiveness and scalability of LOGIC.