Understanding disease mechanisms is crucial for drug discovery, necessitating the integration of diverse and multi-modal data. Here, the authors develop iPANDDA, a network-based computational pipeline that integrates multi-modal data to predict drug targets for SOX2-dependent lung squamous cell carcinoma, identifying and validating key targets like AKT and mTOR complexes.