Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency

ABSTRACT Oxaliplatin (OXA), a critical third‐generation platinum chemotherapeutic, is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery systems. To address this, we developed an integrated machine learning (ML) and multi‐objective optimization (MOO) framework for the simultaneous prediction and exploration of loading efficiency (LE) and encapsulation efficiency (EE). Ensemble learning models, trained on a curated dataset of 70 experimentally characterized nanocarrier formulations, demonstrated robust predictive performance under stringent leave‐one‐paper‐out (LOPO) cross‐validation ( R 2 = 0.87 for LE, R 2 = 0.84 for EE). The multi‐objective exploration identified a Pareto‐optimal design space, with predicted performance reaching up to 45.3% LE and 87.2% EE, and pinpointed a balanced knee‐point formulation at 40.2% LE and 83.7% EE. Interpretable ML analysis revealed surface area‐to‐volume ratio, coordination site availability, and zeta potential as the primary physicochemical drivers of OXA loading and retention. Consequently, an optimized nanocarrier profile, characterized by a particle size of 90–110 nm, a negative surface charge, and a carboxylate‐rich composition, was derived. This study establishes a predictive, data‐driven computational framework that bridges the gap between single‐objective prediction and the holistic design of high‐performance nanocarriers, providing a rational blueprint for accelerating the development of more effective OXA‐based nanotherapies for colorectal cancer.