AI4Loop: an Artificial Intelligence Framework Reveals Increased 3D Chromatin Interactions and Therapeutic Vulnerabilities across 12,000 Cancer Samples

Three-dimensional chromatin interactions shape gene regulation, but their large-scale analysis remains limited by the cost and complexity of experimental assays. Here we present AI4Loop, a deep learning framework that infers genome-wide gene-centered chromatin interaction networks directly from RNA-seq data. Across multiple cell types, AI4Loop recovered interaction patterns consistent with clinical samples and orthogonal chromatin conformation datasets. Applied to 12,347 transcriptomes from 32 cancer types, AI4Loop revealed pervasive increases in gene-centered chromatin interactions in tumors, particularly at oncogene-associated loci. These inferred interaction networks outperformed gene expression alone in cancer classification. Integration with more than 50,000 drug-treated transcriptomes identified compounds predicted to reverse cancer-associated interaction gains. Hi-C experiments confirmed that the oxazolidinone antibiotics eperezolid and radezolid reduce breast cancer-gain chromatin interactions. Together, these results identify increased gene-centered chromatin interactions as a pan-cancer feature and provide a scalable strategy for linking 3D genome dysregulation to therapeutic vulnerabilities.