Abstract
Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine.
Highlights
Introduction their environmentevaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines
ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta-analyses leading to a substantial contribution to safe-bythese advancements bring about in material design and formulation.[1]
One of the first difficulties encountered in this domain pertains to how we organize and utilize the massive volume of information that is being produced, in relation to the performance and environmental and health and design development in nanotoxicology/nanomedicine are mainly focused
Summary
Various ML tools are already being used for nanotoxicity analyses, the comparison or correlation of various studies. To understand the interaction of nanomaterials with biomolecules, the biological surface adsorption index (BSAI) approach has been reported in the literature.[82] This approach characterizes the adsorption properties of the NPs by quantifying the competitive adsorption of a set of small-molecule probes onto the NPs by mimicking the molecular interactions of the NPs with the amino acid residues of the proteins The basis of this approach is the forces that are dominant at that scale, i.e., the Coulomb force, hydrogen bonds, lone-pair repulsions, and London dispersion forces.[85] By measuring the quantities of the probe compounds adsorbed, and their concentration in the surrounding media, the adsorption coefficient (k) is calculated as shown in Figure 6.[82]. Evolutionary algorithms can further optimize the clustering of nanodescriptors and predict the particle properties
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