Role of Artificial Neural Networks in Pharmaceutical Sciences

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.