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

arXiv: https://arxiv.org/abs/2412.07778 · HTML: https://mlsanity.com/paper/2412.07778/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Protein-Ligand Scoring with Convolutional Neural Networks](https://mlsanity.com/paper/1612.02751/) (2016)
- [Simple and Asymmetric Graph Contrastive Learning without Augmentations](https://mlsanity.com/paper/2310.18884/) (2023)
- [PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions](https://mlsanity.com/paper/2008.12249/) (2020)
- [Adaptive Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2305.10837/) (2023)
- [Dual-view Molecule Pre-training](https://mlsanity.com/paper/2106.10234/) (2021)
- [BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions](https://mlsanity.com/paper/2101.12547/) (2021)
- [Associative Learning Mechanism for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2205.15364/) (2022)
- [HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2404.10561/) (2024)

## Built on by (most cited first, 2 of 2)

- [ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2609.02549/) (2026)
- [Towards AI-Driven Nanomedicine Discovery: A Benchmark and Multimodal Learning Framework for Nano Self-Assembly Prediction](https://mlsanity.com/paper/2609.04278/) (2026)

## Abstract (arXiv)

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.