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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.18583 |
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| _version_ | 1866911212572246016 |
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| author | Shen, Zhenqian Zhou, Mingyang Zhang, Yongqi Yao, Quanming |
| author_facet | Shen, Zhenqian Zhou, Mingyang Zhang, Yongqi Yao, Quanming |
| contents | Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Current evaluation often neglects these changes, relying on unrealistic i.i.d. split due to the absence of drug approval data. Results: We propose DDI-Ben, a benchmarking framework for emerging DDI prediction under distribution changes. DDI-Ben introduces a distribution change simulation framework that leverages distribution changes between drug sets as a surrogate for real-world distribution changes of DDIs, and is compatible with various drug split strategies. Through extensive benchmarking on ten representative methods, we show that most existing approaches suffer substantial performance degradation under distribution changes. Our analysis further indicates that large language model (LLM) based methods and the integration of drug-related textual information offer promising robustness against such degradation. To support future research, we release the benchmark datasets with simulated distribution changes. Overall, DDI-Ben highlights the importance of explicitly addressing distribution changes and provides a foundation for developing more resilient methods for emerging DDI prediction. Availability and implementation: Our code and data are available at https://github.com/LARS-research/DDI-Bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18583 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes Shen, Zhenqian Zhou, Mingyang Zhang, Yongqi Yao, Quanming Machine Learning Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Current evaluation often neglects these changes, relying on unrealistic i.i.d. split due to the absence of drug approval data. Results: We propose DDI-Ben, a benchmarking framework for emerging DDI prediction under distribution changes. DDI-Ben introduces a distribution change simulation framework that leverages distribution changes between drug sets as a surrogate for real-world distribution changes of DDIs, and is compatible with various drug split strategies. Through extensive benchmarking on ten representative methods, we show that most existing approaches suffer substantial performance degradation under distribution changes. Our analysis further indicates that large language model (LLM) based methods and the integration of drug-related textual information offer promising robustness against such degradation. To support future research, we release the benchmark datasets with simulated distribution changes. Overall, DDI-Ben highlights the importance of explicitly addressing distribution changes and provides a foundation for developing more resilient methods for emerging DDI prediction. Availability and implementation: Our code and data are available at https://github.com/LARS-research/DDI-Bench. |
| title | Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.18583 |