Things once done in laboratories can now be done with computers and AI, said Kim Woo-youn, CEO and cofounder of Hits Inc. “We live in the age of ‘digital alchemy,’” Kim told BioWorld, describing how AI is shifting some drug discovery processes from physical to virtual spaces.

AI Proteins Inc. has closed a $41.5 million series A financing round to accelerate AI-driven design and development of purpose-built miniprotein medicines across therapeutic applications.

Harbour Biomed is stepping up its antibody discovery process by using AI to develop innovative therapeutics. “We have done great through the traditional way of generating leads and designing molecules, but there’s a major gap as some therapeutics cannot reach the desired location or common targets,” Harbour Biomed founder, chairman and CEO Jingsong Wang told BioWorld.

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.