[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84248-en":3,"doc-seo-84248-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84248,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions","Graph neural networks can predict polypharmacy side effects, yet standard binary cross-entropy training treats easy and difficult examples similarly, risking the omission of clinically significant drug-drug interactions. This work tests whether an asymmetric focal loss objective improves multi-relational DDI prediction by emphasizing difficult positive interactions. ClinicalFocal loss is integrated into a relation-aware graph convolutional network using molecular fingerprints, physicochemical descriptors, and learned embeddings, then evaluated on TWOSIDES with five-fold cross-validation against a binary cross-entropy baseline.","Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions  \nFaranak Hatami1, Mousa Moradi2, *  \n1 Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607, USA; [fhatami@uic.edu](fhatami@uic.edu)  \n2 Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA; [mmoradi2@meei.harvard.edu](mmoradi2@meei.harvard.edu)  \n* [Correspondence: mmoradi2@meei.harvard.edu](Correspondence: mmoradi2@meei.harvard.edu)  \nHighlights  \nWhat are the main findings?  \n• ClinicalFocal loss improved accuracy, F1 score, AUROC, and AUCPR with superior convergence stability compared with binary cross-entropy.  \n• The asymmetric objective achieved 90.9% recall, reduced the false-negative rate from 29.8% to 9.1%, and increased specificity from 69.6% to 87.5% .  \nWhat are the implications of the main findings?  \n• Emphasizing difficult positive interactions improves DDI prediction without architectural changes.  \n• ClinicalFocal loss enables safety-oriented DDI screening; external validation and calibration assessment are needed.  \nAbstract  \nBackground: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to wellclassified and difficult examples, potentially missing clinically significant interactions. We evaluated whether an asymmetric focal objective could improve multi-relational drugdrug interaction (DDI) prediction by emphasizing difficult positive interactions. Methods: ClinicalFocal loss was integrated into a relation-aware graph convolutional network using molecular fingerprints, physicochemical descriptors, and learned embeddings. The model was evaluated on TWOSIDES using five-fold cross-validation with identical experimental conditions (architecture, features, data partitions, hyperparameters, and random seeds) for ClinicalFocal loss and binary cross-entropy baseline. Results: ClinicalFocal loss increased accuracy from 0.699 to 0.892 (+19.3 percentage points) and F1 score from 0.700 to 0.894 (+19.4 percentage points) . AUROC increased from 0.766 to 0.914, and AUCPR increased from 0.714 to 0.860. The falsenegative rate decreased from 29.8% to 9.1%, while specificity increased from 69.6% to 87.5% . Overall classification error decreased from 30.1% to 10.8%, corresponding to a 64.1% relative reduction. Improvements were consistent across all five folds. Conclusions: Asymmetric focal optimization improved classification and ranking performance while achieving 90.9% recall for observed interaction triples, without modifying the underlying architecture. Loss-function design is a direct, tunable lever for improving graph-based DDI prediction.  \nKeywords: drug-drug interactions; polypharmacy; graph neural networks; focal loss; patient safety  \n1. Introduction  \nThe simultaneous use of multiple medications (polypharmacy) is increasingly common in managing older adults and patients with multiple chronic conditions [1,2] . Adverse drug events account for approximately 110,000 deaths annually in the United States [3], making them a leading cause of preventable harm, yet the number of possible drug combinations far exceeds what can be prospectively evaluated in clinical trials [4] . Adverse drug events therefore remain an important source of emergency care and hospitalization, particularly among older adults and patients receiving complex medication regimens [1,2,5]. As medication use increases, identifying potentially harmful drug-drug interactions (DDIs) before they produce clinically significant consequences becomes a critical computational and clinical challenge.  \nBecause exhaustive experimental assessment of all possible drug combinations is infeasible, computational methods have emerged as a practical strategy for detecting and prioritizing potential DDIs [6] . Graph-based machine-learning methods are especially suitable for this pro","cbCaiaHOVltHbTBB","https://ap.wps.com/l/cbCaiaHOVltHbTBB","pdf",1049301,4,1,14,"English","en",105,"# Highlights\n## Main findings\n## Implications\n# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address in drug-drug interaction prediction?\",\"answer\":\"Standard binary cross-entropy training does not focus learning on difficult positive interactions, which can reduce the ability to detect clinically significant DDI signals.\"},{\"question\":\"How does ClinicalFocal loss change the training objective?\",\"answer\":\"ClinicalFocal loss is an asymmetric focal objective that emphasizes difficult positive interactions, improving both classification and ranking without changing the underlying architecture.\"},{\"question\":\"What performance improvements are reported over the baseline?\",\"answer\":\"ClinicalFocal loss increases accuracy and F1, improves AUROC and AUCPR, reduces the false-negative rate, and raises specificity, with consistent gains across five 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problem does the paper address in drug-drug interaction prediction?","Question",{"text":75,"@type":76},"Standard binary cross-entropy training does not focus learning on difficult positive interactions, which can reduce the ability to detect clinically significant DDI signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ClinicalFocal loss change the training objective?",{"text":80,"@type":76},"ClinicalFocal loss is an asymmetric focal objective that emphasizes difficult positive interactions, improving both classification and ranking without changing the underlying architecture.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvements are reported over the baseline?",{"text":84,"@type":76},"ClinicalFocal loss increases accuracy and F1, improves AUROC and AUCPR, reduces the false-negative rate, and raises specificity, with consistent gains across five 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