ABSTRACT
Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as 'Artificial intelligence', 'Pharmaceutical research', 'drug discovery', 'clinical trial', 'disease diagnosis', etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies;Bayesian nonparametric models are the potential technologies for clinical trial design;natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.
ABSTRACT
BACKGROUND: The process of drug discovery and development is expensive, complex, timeconsuming, and risky. There are different techniques involved in the process of drug development, including random screening, computational approaches, molecular manipulation, and serendipitous research. Among these methods, the computational approach is considered an efficient strategy to accelerate and economize the drug discovery process. OBJECTIVE: This approach is mainly applied in various phases of the drug discovery process, including target identification, target validation, lead identification, and lead optimization. Due to the increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling, and pharmacophore mapping have been applied extensively. METHODS: Various drug molecules can be designed by applying computational tools to explore the drug candidates for the treatment of Coronavirus infection. The World Health Organization announced the coronavirus disease as COVID-19 and declared it a global pandemic on 11 February 2020. Therefore, it is thought of interest to the scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA-approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc., for effective treatment of COVID-19 infection. RESULTS: Further, various survey reports suggest that extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates. CONCLUSION: This review is focused on the study of various aspects of these drugs related to their target sites on the virus, binding interactions, physicochemical properties, etc.