Design of Medicinally Pertinent Multifunctional Inorganic Nanomaterials Using Artificial Intelligence

摘要

Over the past decade, artificial intelligence (AI) approaches are dominating the globe, supplementing the use of computational techniques in characterising the features of nanomaterials. Conceivably adhering to the widespread prevalence of technology in the modern world, computing is now an expansive area that provides numerous possibilities for nanotechnology, including networking, visual programming, processing of images, virtual reality, human–machine interface, neural networks (NNs), and autonomous devices. This chapter provides an overview of the design and medicinal applications of multifunctional inorganic nanomaterials adopting AI. Multifunctional materials are material-based systems that have been engineered to fulfil numerous applications through the careful integration of diverse functionalities. In this vantage point, we provide an in-depth discussion of the components involved in the design of nanomaterials, covering topics like machine learning (ML), NNs, deep learning (DL), convolutional neural networks (CNN), computer vision, and robotics, alongside the steps engaged in the design of inorganic nanomaterials by employing AI. The forthcoming healthcare paradigm must shift from oblique, reactive, or even preventive to proactive. In the preceding chapter of this book, we provide a brief overview of the integration of nanotechnology and AI to produce different types of multifunctional nanomaterials such as nanoparticles, carbon nanotubes, quantum dots, nanowires and dendrimers for advanced use in medicine. We have also presented and analysed the present-day concerns and future perspectives of AI-designed nanomaterials in medicine. Overall, AI-enabled multifunctional inorganic nanomaterials have displayed substantial potential in drug delivery and gene therapy, as well as in improving efficiency and diagnostic accuracy.