Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Abstract

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Figure 1

AI applications in cancers.

Figure 2

Tumors and organs at danger are automatically identified.

Figure 3

AI in primary and secondary medical screening: monitoring of possible lead molecules is critical in the drug development and formulation pipeline, and AI plays a key role in defining different and possible drug targets. Chemistry universe has roughly 106 million chemical structures derived from OMIC research, clinical and preclinical studies, in vivo experiments, and microarray analyses. Such molecular and biochemical data are tested out using ML methods like logistic models, reinforcement models, and generative models based on supervised regions, shape, and targeted affinity. The entire diagnostic approach using AI will require 14 to 18 years, which really is significantly less time than classical drug discovery. The first stage in drug development process is leading identification, which involves reverse docking, bioinformatics analytics, and computational chemical biology to identify disease-modifying target proteins. The second phase involves screen chemicals for possible lead molecules that can block the target sequence. It can be accomplished by screening and design from scratch. With targeted library design, substance analysis, drug-target repeatability, and computerized bioinformatics, the next phase throughout the drug development process is leads optimization and leading compound identification. Following that, substances are subjected to secondary screening, which is continued by preclinical studies. Clinical trial, which includes cell-culture evaluation, animal model testing, and patient evaluation, is the final stage in the drug development process [133].