ABSTRACT

The primary domain in the field of pharmaceutical research and development is the process of discovering drugs, wherein artificial intelligence methods and approaches such as machine learning are implemented. Deep Learning, in particular, represents an emerging category within machine learning that is focused on computing technology to replicate the intellect of humans using Artificial Neural Networks. The drug discovery strategy may entail insufficient effectiveness, imprecise delivery, time investment, and a significant financial outlay that could lead to challenges and problems. The Deep Learning technique has demonstrated its strength when dealing with complicated and massive data that contains all the minute details about the drug discovery process. This approach is used in drug property prediction, protein engineering, drug-target interaction prediction, de novo drug design, expression of genes, as well as data analysis. This also includes generating leads, optimisation, validation, as well as preclinical investigations. Despite all of the benefits that accompany the use of deep learning, the limitations and problems encountered during the process. It is primarily a still-developing sector of technology that could potentially be evolved into an exceptionally advanced and effective way that can be utilised in the process of drug development, which may be extremely beneficial to humanity. Keywords: Artificial Intelligence, Machine Learning (ML), Deep Learning (DL), Drug Discovery, Neural Networks, and Protein Engineering
Publication date: 15/10/2023

https://ijbpas.com/pdf/2023/October/MS_IJBPAS_2023_OCTOBER_SPCL_1013.pdf

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https://doi.org/10.31032/IJBPAS/2023/12.10.1013