MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction

TL;DR

MOTIVE introduces a new graph dataset combining Cell Painting features and drug-target interactions, enabling more accurate inductive link prediction for drug discovery.

Contribution

The paper presents MOTIVE, a novel dataset integrating morphological features with interaction data, and demonstrates improved prediction performance using graph neural networks.

Findings

Graph neural networks with Cell Painting features outperform structure-only models.

MOTIVE dataset enables realistic evaluation with various data splits.

Features improve drug-target interaction prediction accuracy.

Abstract

Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein target, cell-based assays such as Cell Painting can better capture complex DTI interactions. This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds, along with their relationships extracted from seven publicly available databases. We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics. MOTIVE accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTIVE resources are available at https://github.com/carpenter-singh-lab/motive.