In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
Abstract
Rapid advancements in nanotechnology have transformed drug design and delivery systems, allowing for precise and efficient therapeutic interventions. This review examines the transformative role of in-silico approaches in nano-drug design, focusing on their ability to predict, optimize, and refine nanomaterial properties for pharmacological applications. Key computational tools such as molecular modelling, machine learning, computational fluid dynamics, and bioinformatics are thoroughly investigated, with a focus on their contributions to understanding drug loading, toxicity, targeting strategies, and nano-bio interactions. Furthermore, the incorporation of emerging technologies like digital twins and quantum computing shows the potential to overcome current limitations in accuracy, scalability, and personalization. Despite significant progress, challenges remain, particularly in closing the gap between computational predictions and experimental validations, dealing with data quality issues, and navigating regulatory frameworks. This review emphasizes the importance of interdisciplinary collaboration and innovation in realizing the full potential of in-silico methods for advancing nanotherapeutics. Addressing these challenges positions the field to accelerate the development of safe, effective, and personalized medicines.
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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