MOTI$\mathcalVE$: A Drug-Target Interaction Graph For Inductive Link Prediction
<|reference_start|>MOTI$\mathcalVE$: A Drug-Target Interaction Graph For Inductive Link Prediction: 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 MOTI$\mathcal{VE}$, a Morphological cOmpound Target Interaction Graph dataset that comprises 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. MOTI$\mathcal{VE}$ accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTI$\mathcal{VE}$ resources are available at https://github.com/carpenter-singh-lab/motive.<|reference_end|>
@article{arevalo2024motive:,
    title={MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction},
    author={John Arevalo, Ellen Su, Anne E Carpenter, Shantanu Singh},
    journal={arXiv preprint arXiv:2406.08649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08649},
    primaryClass={cs.LG}
}