USE OF ARTIFICIAL INTELLIGENCE IN NOVEL DISSOLUTION ENHANCEMENT TECHNIQUES

Registration ID: IJNRD_214108

Published ID: IJNRD2402255

DOI: Click Here to Get

AKSHAY BABAN JADHAV , Indrajeet D. Gonjari , Pranita K. More , Divya B. Jagdale , Shreevidya M. Yamatkar

Artificial Intelligence, Artificial Neural Networks (ANNs), Dissolution Techniques, Theories of Dissolution, Long Short-Term Memory Networks (LSTMs), EDRM, RLRM

Intelligent modelling, which aids in knowledge conception, problem solving, and decision making, is the aim of artificial intelligence (AI). The first studies of what is now called artificial intelligence (AI) were carried out in 1943 by Walter Pits and Warren McCulloch. It used to be believed that artificial intelligence (AI) was only used in engineering. However, artificial intelligence (AI) is now widely applied in the pharmacy sector across a wide range of areas, including hospital pharmacy, drug discovery, formulation development for drug delivery, marketing, management, and marketing. Artificial neural network (ANN) techniques are widely used in pharmaceutical research for data filtering and experimental outcome prediction. Regression analysis and a backpropagation network were used in this work to model a new dissolution outcome prediction and screen system. Drug research and the development of drug delivery formulations involve a large number of Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs). The technology's promise in quantitative structure-property relationship (QSPR) or quantitative structure-activity relationship (QSAR) has been validated, and several drug discovery implementations have been studied. De novo design also promotes the creation of pharmacological compounds with significantly novel properties in terms of intended or ideal attributes. Robots are used by doctors nowadays for a range of medical procedures since they are more dependable than people, have more sophisticated work features, and can finish any task swiftly and effectively. We draw the conclusion that artificial intelligence (AI) is the burgeoning subject that is affecting every business, including pharmacy, and that more work must be done in this area in order to advance the state of the art and carry out fresh investigations. Because of the way the input data was prepared, the relevant data could still be used to train the ANN model even after the formulation composition was changed. This system uses the reference line regression methodology (RLRM) and the effective data regression method (EDRM) to predict dissolution results with a high accuracy rate. Nevertheless, compared to the orthogonal experiment, it requires a smaller database. Additionally, this system implements a decision tree-based data screen function. The drug prediction system created by this artificial neural network (ANN) model is distinct.

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AKSHAY BABAN JADHAV, Indrajeet D. Gonjari, Pranita K. More, Divya B. Jagdale, & Shreevidya M. Yamatkar (February-2024). USE OF ARTIFICIAL INTELLIGENCE IN NOVEL DISSOLUTION ENHANCEMENT TECHNIQUES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(2), c487-c500. https://ijnrd.org/papers/IJNRD2402255.pdf

Volume 9 Issue 2, February-2024

Pages : c487-c500

Paper Reg. ID: IJNRD_214108

Published Paper Id: IJNRD2402255

Downloads: 000121987

Research Area: Pharmacy

Country: Satara, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402255.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402255

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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