Abstract

Artificial Intelligence is an algorithm based computational approach to find solution from existing data. It has wider applications in different areas like medicine, agriculture, etc., and pharmaceutical field is not out of its ambit. Machine learning (ML) that is a subset of artificial intelligence, plays a crucial role in drug discovery and development by employing a massive quantum of structured and semi-structured data so that a ML model can create accurate outputs or provide predictions based on the data under analysis. It is a technical approach in which the machines are trained to process a significant amount of data. Two main categories of ML are supervised and unsupervised learning which are the platforms for the data processing. ML uses historical data which is responsible to generate algorithms for its working to produce the output. This can be accomplished in the ligand-based and structure-based approaches of drug design that helps to anticipate the hit and lead molecules for the drug discovery process.

Keywords: Artificial Intelligence; Machine Learning; Supervised Learning; Unsupervised Learning; Algorithms

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