Advancing precision oncology: a critical review of in Silico tools for nanoparticle-based drug delivery systems

The advent of in silico approaches has added a new dimension to the design of nanoformulations for targeted drug delivery, particularly in cancer therapy. Such computer simulation-based methodologies offer an economic and efficient means for the targeted design and optimisation of drug delivery systems. With the growing interest in computational nanoformulations, owing to their potential to increase the delivery, bioavailability, and therapeutic efficacy of anticancer drugs, scaffolding tools in silico have become essential for their rational design. Through the simulation of molecular interactions, in silico models provide essential information on the dynamics of nanoparticles in biological systems, allowing the prediction and optimisation of their target efficiency and therapeutic efficacy. These computation-based methodologies are also essential for the design of personalised medicine, as they can reduce the systemic toxicity and side effects of conventional cancer therapies. The integration of in silico methodologies into drug nanoformulation research is a significant step toward more efficient, patient-specific therapy. Despite their significant potential, several challenges, including limited model validation, biological complexity, protein corona formation, specific ADMET prediction tools, and regulatory uncertainty, hinder clinical translation. This review illustrates the potential of in silico research in the advancement of cancer treatment development using nanoformulations. This paper presents a critical review of existing computational tools, such as molecular dynamics simulations, molecular docking, machine learning, and AI-based platforms, applied in the design, screening, and optimisation of nanomedicines. It also illustrates the application of key computational tools and platforms, such as GROMACS, AutoDock, Schrodinger, and Gaussian, and web-based platforms, such as SwissADME and pkCSM. Together, these tools facilitate the design of efficient and targeted drug delivery systems, allowing for their eventual clinical translation. In silico tools revolutionize the rational design of cancer-targeted nanoformulations. Molecular docking unveils precise ligand–nanoparticle–receptor binding mechanisms. MD simulations decode NP behavior under physiological and tumor conditions. AI-driven modeling accelerates prediction of NP efficacy and biocompatibility. Integrated ADMET tools streamline early screening of nanoformulated anticancer drugs.