Efficient Correlated Subgraph Searches for AI-powered Drug Discovery

Efficient Correlated Subgraph Searches for AI-powered Drug Discovery

Hiroaki Shiokawa, Yuma Naoi, Shohei Matsugu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2351-2361. https://doi.org/10.24963/ijcai.2024/260

Correlated subgraph searches (CSSs) are essential building blocks for AI-powered drug discovery. Given a query molecule modeled as a graph, CSS finds top-k molecules correlated to the query in a database. However, the cost increases exponentially with the molecule size. Herein we present Corgi, a framework to accelerate CSS methods while ensuring top-k search accuracy. Corgi dynamically excludes unnecessary subgraphs to overcome the expensive cost without sacrificing search accuracy. Our experimental analysis confirms that Corgi has a shorter running time and improved accuracy compared to existing state-of-the-art methods, while a case study demonstrates that Corgi is suitable for practical AI-powered drug discovery.
Keywords:
Data Mining: DM: Mining graphs
Data Mining: DM: Big data and scalability
Data Mining: DM: Applications
Multidisciplinary Topics and Applications: MTA: Other