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Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications

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Abstract

Nanoscience in healthcare offers significant advancement in the areas of diagnostic and therapeutic for imaging, biosensing, targeted drug delivery systems, etc. To extend the applications in biomedical engineering, artificial intelligence (AI) technology holds the power to analyze and interpret biological data, accelerate drug discovery and identify selective small molecules or unique compounds with predictive behavior. Implementation of such database systems for rapid data analysis, treatment strategies, novel hypotheses development, and determination of disease progression remarkably improves the treatment outcomes with the potential to accelerate the high-throughput development and systematic design of highly effective smart materials and nanoformulations with pre-defined functionality. Specifically, optimizing physicochemical parameters, compatibility, and drug-dose parameters with higher prediction efficiency (above 90%) is the area where AI holds the potential to actionably cognize the full nanotechnology potential. This review article discusses the research findings to accelerate the clinical translation of nanoscience, bestow the potential development of high throughput experimentation-based, AI-assisted design, and data-driven production of nanosynthesized systems.

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Abbreviations

AI:

Artificial intelligence

ML:

Machine learning

DL:

Deep learning

NN:

Neural network

ANN:

Artificial neural network

DNN:

Deep neural network

CNN:

Convolutional neural network

SPM:

Scanning probe microscopy

PCA:

Principal component analysis

PCL:

Poly(L-lactide)

PLGA:

Poly(lactic-co-glycolic acid)

EC:

Ethyl cellulose

PLA:

Polylactic acid

PEG:

Polyethylene glycol

PVA:

Polyvinyl alcohol

DLC:

Drug loading content

DLE:

Drug loading efficiency

DG:

Docking binding energy

RMSE:

Root mean square error

MAE:

Mean absolute error

HLB:

Hydrophilic lipophilic balance

DOX:

Doxorubicin

MLP:

Multi-layer perceptron

MON-MLP:

Monotome multi-layer perceptron

QSPR:

Quantitative structure property relationships

QSAR:

Quantitative structure activity relationship

SVR:

Support vector regression

HCA:

Hierarchical clustering algorithm

LDA:

Linear discriminant analysis

kNN:

K-nearest neighbors

HUVEC:

Human umbilical vein endothelial cells

GANDA:

Generative adversarial network for distribution analysis

QD:

Quantum-dot

ROI:

Region of interest

MLR:

Multiple linear regression

SERS:

Surface-enhanced raman spectroscopy

HCG:

Human chronic gonadotropin

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  1. Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’S NMIMS, V. L. Mehta Road, Vile Parle (W), Mumbai, India

    Kaustubh Kasture & Pravin Shende

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  1. Kaustubh Kasture
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  2. Pravin Shende
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Kasture, K., Shende, P. Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications. Arch Computat Methods Eng 30, 4667–4685 (2023). https://doi.org/10.1007/s11831-023-09948-3

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