Abstract This study integrates mathematical modelling and experimental validation to investigate the fundamental interactions between the chemotherapeutic agent doxorubicin (DOX) and graphene oxide (GO), aiming to advance targeted drug delivery. Our theoretical approach, using the Lennard–Jones potential and the unified non-dominated sorting genetic algorithm III, successfully predicts the energetic landscape and optimal binding configurations of the DOX–GO system. These computational results are directly supported by our experimental findings on drug-loading capacity. We observe that a higher degree of GO oxidation leads to weaker interaction energy, a conclusion consistent with both our theoretical predictions and empirical data. This comprehensive methodology provides a molecular-level understanding of DOX–GO interactions, enabling the rational design of more efficient and therapeutically effective GO-based drug delivery platforms.
Katewongsa et al. (Wed,) studied this question.