ABSTRACT
The practice of treating several diseases affecting mankind has been a long process. The first step in curing a disease involves diagnosis of the diseases through the application of refined machines to usage of details gleaned from biomarkers acquired by the most thrillingly invasive of resources. Since the inception of the earth, several scientists have recorded numerous success stories from the application of allopathy as a means of treatment for the past 100 years as a replacement of homeopathy and practices of medical purgatory. However, several demerits are associated with these techniques as well as those recorded with the bioinformatics and omics techniques which have prompted precision medicine as a platform embracing the molecular profile of specific patients drives the choice of treatment. The advancement in artificial intelligence has allowed the automation of novel invention in chemical entities as well as enormous databases in health-privacy-protected crypts. Several interdisciplinary approaches have been combined to embrace the importance of recurrent neural networks and transfer learning for effective designing of new drug candidates, which gives better information on the safety, toxicity, metabolism, and engineering for the delivery of active components. Moreover, it has been observed that artificial neural networks exhibited its first success in drug discovery and molecular informatics about two decades ago. Therefore, there is a need to make several advancements in neural network architectures most especially for pharmaceutical research through the application of deep learning which is highly relevant to advances in the pharmaceutical and chemoinformatics research. Thus, leveraging on mining the dissimilar big data which accrues by means of refined algorithms such as deep learning was also highlighted.
