In the past decade, hundreds of drugs were withdrawn from the market as a result of their failure to either exhibit the desired biological activity or due to their unexpected toxicity and adverse side effects in clinical trials. These poor outcomes may be attributed due to lack of a planned and systematic approach to rational drug design and discovery leading to losses that place a heavy burden on the poor economic sections of the society in particular and the third world countries in general. In the post genomic era, in silico prediction of chemical leads has a significant role in drug discovery, which has proven to be more time and cost effective (Xu et al., 2012). Clinical studies of various drug like properties are very time consuming and expensive. Therefore, computational techniques come is as helpful and handy before going for the expensive in vitro, in vivo studies and clinical trials. Computational techniques can filter and predict the druglikeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria of a prospective drug candidate (Cheng et al., 2012). In this context, Ayurveda, the Indian traditional system of medicine encompassing a plethora of medicinal plants and their phytoconstituents(s) with proven diverse pharmacological activities, has remained largely unexplored. Thus, Ayurveda offers a staggering array of natural products and prospective therapeutic agents for evaluation against the causal agent of the ongoing global pandemic.