Role of Artificial Neural Networks in Pharmaceutical Sciences

    Published on:February 2022
    Journal of Young Pharmacists, 2022; 14(1):6-14
    Review Article | doi:10.5530/jyp.2022.14.2

    Talasila Bhanu Teja1, Mahendran Sekar2,*, Talasila Pallavi3, Sivamma Mettu4, TE Gopalakrishna Murthy5,*, Nur Najihah Izzati Mat Rani6, Pallaval Veera Bramhachari7, Srinivasa Reddy Bonam8

    1School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, INDIA.

    2Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Health Sciences, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh, Perak, MALAYSIA.

    3Department of Computer Science and Engineering, KL University (Deemed to be University), Guntur, Andhra Pradesh, INDIA.

    4Independent Researcher, Île-de-France, Paris, FRANCE.

    5Department of Pharmaceutics, Bapatla College of Pharmacy, Bapatla, Guntur, Andhra Pradesh, INDIA.

    6Faculty of Pharmacy and Health Sciences, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh, Perak, MALAYSIA.

    7Department of Biotechnology, Krishna University, Machilipatnam, Andhra Pradesh, INDIA.

    8Institut National de la Santé et de la Recherche Médicale; Centre de Recherche des Cordeliers, Equipe-Immunopathologie et Immunointervention Thérapeutique, Sorbonne Université, Université de Paris, Paris, FRANCE.


    Artificial Neural Networks (ANN) are becoming the tool of choice for the pharmaceutical industry due to their ability to mimic the brain’s way of functioning. Computational and statistical methods have recently sparked the pharmaceutical industries interest in identifying possible pharmaceutical products that fulfil all technical requirements. Researchers are developing ANN from various scientific areas to overcome prediction, optimization, recognition, and control problems. Conventional techniques can only be used in specific, well-constrained situations. ANN analyses incomplete or unstructured information and converts it into more sensible analysable data by detecting the underlying patterns and similarities. This technology also creates new ideas by rearranging existing knowledge. For example, ANN can construct a promising modelling technique when data sets exhibit nonlinear correlations, which is prominent in pharmaceutical operational processes. In the pharmaceutical industry, this thinking network can be used in disease diagnosis, genomics and proteomics, drug design, to determine physicochemical properties of a drug, drug testing, optimization, pharmacokinetics, in vitro and in vivo correlations, and also to study drug interactions. In this short review article, various applications of ANN in pharmaceutical research are presented.

    Key words: Artificial Neural Networks, Pharmaceutical Sciences, Drug Design, Drug Discovery, Drug Delivery, Formulation Development.

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