South Korean researchers led by Lee In-suk of Yonsei University have reported the most complete oral microbiome catalog to date, with more than 72,000 genomes. Detailed in Cell Host & Microbe on Nov. 12, 2025, the database is expected to serve as a universal platform for academia and enable “precision microbiome medicine” for the industry, Lee told BioWorld.

GSK plc and the Fleming Initiative have announced six major new research programs to find new ways to slow the progress of antimicrobial resistance (AMR). The Fleming Initiative is a collaboration established by Imperial College London and Imperial College Healthcare NHS Trust to help tackle AMR. Each of the new programs will begin by early next year and are fully funded for 3 years.

South Korean researchers led by Lee In-suk of Yonsei University have reported the most complete oral microbiome catalog to date, with more than 72,000 genomes. Detailed in Cell Host & Microbe on Nov. 12, 2025, the database is expected to serve as a universal platform for academia and enable “precision microbiome medicine” for the industry, Lee told BioWorld.

A technology that combines transcriptomic data and AI enables a novel approach to drug discovery based on the state of cells, how they behave and which genes they express. The Drugreflector model, developed by scientists at Cellarity Inc., learns from gene expression profiles and predicts which compounds could induce beneficial changes in that cellular state to develop a treatment.

John Squires, the recently anointed director of the U.S. Patent and Trademark Office, has determined that a machine learning (ML) patent application met the standard for patent subject matter eligibility, an outcome that seems to bode well for ML-based patent applications going forward.

A team of U.S. and South Korean researchers have developed an AI model called MSI-SEER that can not only predict microsatellite instability-high (MSI-H) tumors based on tissue slides, but also flag “what it does not know.” “Have you ever asked ChatGPT anything, and the response was, ‘I don’t know?’” Cheong Jae-ho asked during an interview with BioWorld. “Probably not, and that is the problem with AI now.”

Pharma companies are collaborating to boost the power of artificial intelligence (AI) in drug discovery by allowing access to proprietary structural data to train a large language model. Each of the partners is contributing data from several thousand experimentally determined protein:ligand interactions, creating one of the most diverse datasets and the richest chemistry assembled to date for model training.

A new generative AI model trained on UK Biobank data can simultaneously forecast the risks and timing of developing over 1,000 different diseases a decade into the future. The developers applied similar algorithmic concepts to those used to develop large language models like ChatGPT and Gemini to build the model, using medical records from 402,799 participants in UK Biobank.

Researchers at the Massachusetts Institute of Technology have developed a generative AI model that was able to generate novel antibiotic structures from either chemical fragments or de novo, starting from ammonia, methane, water or no starting point at all. In a study that was published online in Cell, the team tested two dozen of more than 10 million structures that were proposed as potential antibiotics by the model.

Deep learning tools for protein design can also be used to create molecules that bind to them. Certain peptides, such as intrinsically disordered proteins (IDPs), are challenging to target due to their variable nature. However, scientists from the lab of Nobel laureate David Baker have developed a method to generate binders for IDPs by searching the world’s largest protein database with their AI-powered tool RFdiffusion.