TIGP (BIO)—AI and Big Data Analytics for Antimicrobial Resistance Detection and Drug Development

Abstract

We have just emerged from the shadow of the COVID-19 pandemic that lasted more than three years. But has this battle against viruses truly come to an end, or is it merely the prelude to an even more formidable challenge?

In fact, as early as 2017, the World Health Organization (WHO) warned that antimicrobial resistance (AMR) would become one of the most severe global public health crises in the coming decades. Unlike a pandemic, this is a “silent war” without visible conflict, yet it continues to spread relentlessly—its threat potentially surpassing that of COVID-19.

Over the past decade, rapid advances in biotechnology—particularly next-generation sequencing (NGS) and mass spectrometry—have enabled high-throughput platforms to generate unprecedented volumes of biological data within a short time. However, the primary challenge has shifted from data generation to knowledge extraction: how to derive interpretable and clinically actionable insights from massive datasets.

This talk will focus on integrating big data analytics with artificial intelligence (AI) to develop clinically applicable models for rapid antimicrobial resistance detection, enabling real-time identification of common pathogens. In addition, we will explore AI-driven strategies to accelerate the development of drugs with reduced resistance risk, offering new solutions to combat multidrug-resistant superbugs.

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