Course Description

How AI Identifies Disease Targets: AI in Predictive Medicine

This course introduces students and life-science professionals to the practical application of Artificial Intelligence in disease-target identification, drug discovery, and predictive medicine. Participants learn how modern AI approaches—including Scientific Prompting, Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI—can support biomedical research without requiring prior coding experience.

The course takes learners through an AI-assisted research workflow, beginning with literature mining and evidence extraction, followed by disease-target identification, pathway analysis, compound evaluation, predictive analytics, and patient-centric predictive medicine concepts.

Using practical case studies such as Curcumin in Oral Cancer, participants explore how AI can identify and prioritise molecular targets, connect compounds with biological pathways, organise biomarkers and endpoints, and convert scientific evidence into structured research outputs.

The course also introduces applications of AI across the broader pharmaceutical development ecosystem, including clinical research, pharmacovigilance, regulatory affairs, scientific reporting, and evidence-based decision-making.

What You Will Learn

Fundamentals of AI, LLMs, Generative AI, RAG and Agentic AI

Scientific prompting and structured prompt design

AI-assisted literature mining and evidence extraction

Disease-target identification and prioritisation

Target, pathway and compound analysis

Predictive analytics for biomedical research

Biomarker and endpoint identification

Fundamentals of predictive medicine

AI applications in clinical research and pharmacovigilance

Scientific report development and portfolio building

Who Should Join

Suitable for Pharmacy, Life Sciences, Biotechnology, Healthcare and Biomedical Science students, researchers and early-career professionals interested in developing practical AI-assisted research skills.

No prior AI, programming or coding experience is required.