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

2024-06-12 · John Arevalo, Ellen Su, Anne E Carpenter, Shantanu Singh

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.

📄 PDF Abstract BibTeX arXiv:2406.08649

Code (1)

carpenter-singh-lab/motive 공식 구현 pytorch

Tasks

Drug Discovery Inductive Link Prediction Link Prediction

A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction

2025-07-15 · Yuehua Song, Yong Gao

Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (G…

Drug Discovery Graph Learning Inductive Learning Transductive Learning

PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps

2023-10-06 · Neural Information Processing Systems 2023 AI for Science: from Theory to Practice Workshop 2023 12 · Rakesh Bal, Yijia Xiao, Wei Wang

Developing and discovering new drugs is a complex and resource-intensive endeavor that often involves substantial costs, time investment, and safety concerns. A key aspect of drug discovery involves identifying novel dru…

Binary Classification Drug Discovery Inductive Bias

Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction

2022-05-12 · Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang

Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding inter…

Gene Interaction Prediction Representation Learning

SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction

2025-03-12 · Yigang Chen, Xiang Ji, Ziyue Zhang, Yuming Zhou 외

Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To addre…

Diversity Drug Discovery

Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction

2025-01-26 · Xiaoqing Lian, Jie Zhu, Tianxu Lv, Shiyun Nie 외

Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations. In contrast, B…

Drug Discovery Meta-Learning