Abstract:Traditional drug discovery methods such as wet-lab testing, validations, and
synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches
have progressed to the point where they can have a significant impact on the
drug discovery process. Using massive volumes of open data, artificial intelligence
methods are revolutionizing the pharmaceutical industry. In the last few decades, many
AI-based models have been developed and implemented in many areas of the drug development
process. These models have been used as a supplement to conventional research to
uncover superior pharmaceuticals expeditiously. AI's involvement in the pharmaceutical
industry was used mostly for reverse engineering of existing patents and the invention of
new synthesis pathways. Drug research and development to repurposing and productivity
benefits in the pharmaceutical business through clinical trials. AI is studied in this article
for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical
sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical
characteristics, among other things. In this review article, we have discussed its application
to a variety of problems, including de novo drug discovery, target structure prediction, interaction
prediction, and binding affinity prediction. AI for predicting drug interactions and
nanomedicines were also considered.