Advances in Drug Discovery based on Genomics, Proteomics and Bioinformatics in Malaria

Malaria is one of the neglected infectious diseases, and drugs are the first line of action taken against the onset of malaria as therapeutics. The drugs can be of either natural or artificial origin. Drug development has multiple impediments grouped under three categories, a. drug discovery and screening, b. the drug's action on the host and the pathogen, and c. clinical trials. Drug development takes coon's age from discovery to the market after FDA approval. At the same time, targeted organisms develop drug resistance quicker than drug approval, raising the requirement for advancement in drug development. The approach to explore drug candidates using the classical methods from natural sources, computation-based docking, mathematical and machine learning-based high throughput in silico models or drug repurposing has been investigated and developed. Also, drug development with information about the interaction between Plasmodium species and its host, humans, may facilitate obtaining an efficient drug cohort for further drug discovery or repurposing expedition. However, drugs may have side effects on the host system. Hence, machine learning and systems-based approaches may provide a holistic view of genomic, proteomic, and transcriptomic data and their interaction with the selected drug candidates. This review comprehensively describes the drug discovery workflows using drug and target screening methodologies, followed by possible ways to check the binding affinity of the drug and targets using various docking software.