Sitemap

Nicole Dale

AlphaGenome Atlas: Mapping Every Possible DNA Variant

Pharmatica image representing the AlphaGenome Atlas genomic AI map with single-nucleotide variant prediction across the human genome.

Google DeepMind’s AlphaGenome Atlas maps all nine billion possible DNA variants, helping researchers rapidly prioritise disease-linked mutations.

Nicole Dale

Machine Learning in Drug Discovery: Gains and Data Gaps

Pharmatica image showing a bright laboratory with a green molecular network and glowing AI node.

Machine learning in drug discovery promises faster drug screening and fewer failures, but weak data limits gains. See what the latest 2026 review means for pharma.

Nicole Dale

AI Biology: What Biohub’s $1.8bn Push Means for Pharma

Pharmatica image of a scientist viewing glowing cell imaging data on a screen in a bright laboratory with blue tones and a yellow AI highlight.

In one of the most important developments, AI biology gets a $1.8 billion boost as Biohub, DOE, and the NIH build open and AI-ready data for virtual cell models.

Nicole Dale

Target Atlas for Smarter Target Prioritisation for Parkinson’s

Pharmatica image of scientific papers, a glass slide, and laboratory glassware on a bright research desk, representing evidence review for Parkinson’s drug target prioritisation.

Target prioritisation gets an evidence upgrade. Find out how Phylo’s Target Atlas and the MJFF Parkinson’s edition rank disease drug targets.

Nicole Dale

Novo Bets on AI Drug Discovery as GLP-1 Patents Near Expiry

Pharmatica image of laboratory workstation where digital molecular data is reviewed on a screen in a bright pharmaceutical laboratory, representing AI drug discovery.

AI drug discovery is moving into strategy at Novo. See its 2026 AI milestones, what they prove, and the questions R&D teams should ask before copying them.

Nicole Dale

The Cursed Little Black Box of the AI Algorithm

Pharmatica minimalist pharmaceutical laboratory bench featuring a central, polished black glass cube glowing with internal digital and molecular patterns, representing the black-box problem in AI-driven drug discovery.

Explore the black-box problem in AI-driven drug discovery, including interpretability, data quality, reproducibility, and the evidence needed to trust predictions.