AI‑Driven Multi‑Omics Integrative Model for Predicting Tumor‑Specific Drug Responses

Abstract

Precision oncology requires robust drug-response prediction because single biomarkers cannot fully explain therapeutic variation across tumors, cell lines, and drug structures. This study develops an AI-driven multi-omics model using GDSC, CCLE, DepMap, CTRP, and TCGA data, integrating mutation, copy-number variation, RNA expression, DNA methylation, drug SMILES, and IC50/AUC responses. Omics features are encoded separately, drug structures are modeled with a graph attention network, and cross-attention learns tumor-drug interactions. The model outperforms Elastic Net, Random Forest, XGBoost, and DeepCDR-style baselines, achieving RMSE of 0.684±0.014 and AUC of 0.872±0.011. Interpretability analysis highlights EGFR/ERBB, DNA repair, cell cycle, and PI3K-AKT pathways, demonstrating that multi-omics fusion and drug-structure modeling improve prediction stability and support interpretable precision-oncology drug screening.