Study on Emerging Machine Learning Trends on Nanoparticles—Nanoinformatics

Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

“…The physicochemical properties of nanomaterials must be encoded as mathematical entities called descriptors, and artificial intelligence (AI) and machine learning (ML) methods should be applied to generate predictive models of the desired property. AI is an influential technology in nanotechnology . ML methods can modulate the time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of nanomaterials .…”

“…Quantitative structure–activity/toxicity relationship (QSAR/QSTR)-based approaches have gained rising attention as safety/risk assessment tools. − Some guidelines for their development have been proposed to overcome regulatory issues . The modern three-dimensional quantitative structure–activity relationship (3D-QSAR) molecular docking can be helpful to forecast molecular characteristics and biological interactions of pharmaceutics (Figure A) . However, 3D QSAR and classical molecular descriptors cannot express the specificity of nanoparticles.…”

“…AI is an influential technology in nanotechnology. 221 ML methods can modulate the time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of nanomaterials. 222 As referred formerly, the physicochemical properties of the nanomaterials are key determinants of their fate in the human body.…”

“…227 The modern threedimensional quantitative structure−activity relationship (3D-QSAR) molecular docking can be helpful to forecast molecular characteristics and biological interactions of pharmaceutics (Figure 14A). 221 However, 3D QSAR and classical molecular descriptors cannot express the specificity of nanoparticles. Moreover, few nano-QSAR modeling approaches have been developed.…”