MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation

TL;DR

The paper introduces MIN, a multi-channel interaction network that improves drug-target interaction prediction by combining representation learning, residue selection, and contrastive learning, achieving superior results and interpretability.

Contribution

MIN is a novel framework integrating multiple interaction channels and a residue screening mechanism, advancing DTI prediction accuracy and explainability.

Findings

MIN outperforms existing DTI prediction methods on public datasets.

Residue selection by C-Score Predictor aligns with actual binding pockets.

MIN provides insights into protein binding site prediction.

Abstract

Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experimental studies, leveraging modern machine-learning techniques to discern patterns between drugs and target proteins has become increasingly feasible. In this paper, we introduce the Multi-channel Interaction Network (MIN), a novel framework designed to predict DTIs through two primary components: a representation learning module and a multi-channel interaction module. The representation learning module features a C-Score Predictor-assisted screening mechanism, which selects critical residues to enhance prediction accuracy and reduce noise. The multi-channel interaction module incorporates a structure-agnostic channel, a structure-aware channel, and an extended-mixture channel, facilitating the identification of interaction patterns at various levels for optimal complementarity. Additionally, contrastive learning is utilized to harmonize the representations of diverse data types. Our experimental evaluations on public datasets demonstrate that MIN surpasses other strong DTI prediction methods. Furthermore, the case study reveals a high overlap between the residues selected by the C-Score Predictor and those in actual binding pockets, underscoring MIN's explainability capability. These findings affirm that MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.