Ahn, GeonheeHaque, Md Mahim AnjumHazarika, SubhashisKim, Soo Kyung2024-11-042024-11-042024-10-21https://hdl.handle.net/10919/121541Despite the powerful capabilities of GNN-based drug screening model in predicting target drug properties, the black-box nature of these models poses a challenge for practical application, particularly in a field as critical as drug development where understanding and trust in AI-driven decisions are important. To address the interpretability issues associated with GNN-based virtual drug screening, we introduce XplainScreen: a unified explanation framework designed to evaluate various explanation methods for GNN-based models. XplainScreen offers a user-friendly, web-based interactive platform that allows for the selection of specific GNN-based drug screening models and multiple cutting-edge explainable AI methods. It supports both qualitative assessments (through visualization and generative text descriptions) and quantitative evaluations of these methods, utilizing drug molecules in SMILES format. This demonstration showcases the utility of XplainScreen through a user study with pharmacological researchers focused on virtual screening tasks based on toxicity, highlighting the framework’s potential to enhance the integrity and trustworthiness of AI-driven virtual drug screening. A video demo of XplainScreen is available at https://youtu.be/Q4yobrTLKec, and the source code can be accessed at https://github.com/GeonHeeAhn/XplainScreen.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2024-11-01The author(s)https://doi.org/10.1145/3627673.3679236