AI embedding intelligence at every stage of healthcare

ARTIFICIAL Intelligence (AI) is no longer a futuristic concept—it's a present-day force redefining how the world discovers, develops, and delivers medicines.Once viewed as a distant dream, AI has now become the catalyst for a scientific revolution, transforming pharmaceutical research into a faster, more precise, and more human-centered enterprise.

- BANDOO C CHATALE, MANSI S GUPTA, PRAJAKTA M NIKATE, SAYYED MASOOD AHMED

A Brief History of Artificial Intelligence

The story of AI began in 1950, when Alan Turing proposed the Turing Test to determine whether machines could think like humans. In 1956, John McCarthy coined the term Artificial Intelligence and developed LISP, the first AI programming language.

The 1970s introduced PROLOG and early Artificial Neural Networks (ANNs) by Kunihiko Fukushima, laying the foundation for machine learning. By the 1990s, advances such as genetic programming expanded Al's problem-solving power.

The 2000s marked the rise of deep learning, enabling machines to learn from vast datasets. In 2017, the U.S. FDA approved the first Al-based medical device, and by 2023, an AI-generated drug for pulmonary fibrosis entered clinical trials. Today, AI symbolises the intersection of human ingenuity and computational intelligence—transforming both medicine and modern life.

AI in Drug Discovery: From Hypothesis to Healing

“AI is compressing what once took decades into months—bringing discovery closer to patients.”

Drug discovery has traditionally been slow, uncertain, and costly, often taking up to 15 years and over a billion dollars. Al has rewritten this story by enabling data-driven innovation that is faster, smarter, and more accurate.

Target Identification: Al tools like Deep-Mind's AlphaFold2 have mapped over 200 million protein structures, revolutionising target-based drug design.

Hit Discovery: Platforms such as Insilico Medicine have screened millions of compounds in hours, developing a fibrosis drug candidate that entered Phase I trials in just 2.5 years.

Lead Optimisation: Machine learning models now balance efficacy, toxicity, and pharmacokinetics, allowing intelligent selection of safe and potent candidates.