AI Drug Discovery– category –
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Multimodal AI Beats Oncotype DX — A New Era for Breast Cancer Recurrence Prediction | Reading Breast Cancer Diagnosis with AI, Vol. 3
At SABCS 2025, multimodal AI (ICM+ model) outperforms Oncotype DX for 15-year recurrence prediction (C-index 0.733 vs 0.631). Final volume of our series. -
AI Triage Cuts Radiologist Workload by 63% — A Partially Autonomous Workflow Demonstrated by AITIC | Reading Breast Cancer Diagnosis with AI, Vol. 2
Spain's AITIC trial (Nature Medicine 2026, n=31,301): partially autonomous AI workflow cuts radiologist workload 63.6%, lifts detection 15.2%. Vol. 2. -
What MASAI Answered — Has AI Mammography Surpassed Radiologists? | Reading Breast Cancer Diagnosis with AI, Vol. 1
Sweden's MASAI trial (Lancet 2026, n>105,000) shows AI mammography cuts interval cancers by 12% and lifts sensitivity to 80.5%. Vol. 1 of our series. -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Extra Chapter, Snapshot as of November 22, 2025) focuses only on the latest AI-driven case studies and summarizes how far AI has actually progressed in real drug discovery and development.
1. Where Does AI in Drug Discovery Stand in 2025? Between 2024 and 2025, AI in drug discovery has quietly moved from the “proof-of-concept” phase into a stage where AI-designed molecules are showing signals in human trials. Between 2024 ... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 7: “Putting It All Together”) summarizes a cross-modality roadmap for AI in drug discovery and development, clarifies what AI can and cannot realistically do, and outlines strategic considerations from lab bench to market access.
1. Recap: A Cross-Modality “AI in Drug Discovery” Map In Parts 1–6, we walked through: What you will learn 1. Recap: A Cross-Modality “AI in Drug Discovery” Map2. Phase-by-Phase Roadmap: From Discovery to Post-Marketing3. Strengths and W... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 6: “Cell & Gene Therapy × AI”) maps how AI is used from CAR-T/TCR and vector design to manufacturing and quality control, and clarifies both its potential and its current limitations in these complex modalities.
1. Why “Cell & Gene Therapy × AI” Is Both Difficult and Important In Part 6, we focus on cell therapies (e.g., CAR-T, TCR-T) and gene therapies (e.g., AAV or lentiviral vectors). Compared with small molecules, antibodies, or RNA ther... -
How Far Can We Trust Genome AI? — Reading AlphaGenome (Nature) with an Implementation Mindset (Expert Edition)
In the beginner edition, we focused on why the “other 98%” of DNA (non-coding regions) is hard to interpret and why genome AI should be framed as hypo In the beginner edition, we focused on why the “other 98%” of DNA (non-coding regions)... -
What Does the “Other 98%” of DNA Do? — A Beginner-Friendly Guide to Genome AI (What DeepMind’s AlphaGenome Suggests)
When people hear “genome analysis,” they often imagine a simple story: read DNA, find a mutation, and explain a disease. In reality, the deeper we read, the more often we hit a frustrating wall: we can detect many variants, but we can’t ... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 5: “Nucleic Acid & RNA Therapeutics × AI”) summarizes how AI is used for sequence design, target selection, delivery optimization, and safety assessment for RNA and nucleic-acid medicines.
1. Why “Nucleic Acid & RNA Therapeutics × AI” Matters mRNA vaccines, siRNA, antisense oligonucleotides (ASOs), saRNA, and CRISPR guide RNAs have rapidly moved from concept to clinical reality. mRNA vaccines, siRNA, antisense oligonuc... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 4: “Antibodies & Biologics × AI”) explores how AI is applied to sequence design, affinity maturation, and developability prediction, and how this differs from small-molecule use cases.
1. Why “Antibodies & Biologics × AI” Is Different from Small Molecules In Part 3, we focused on small-molecule projects. In Part 3, we focused on small-molecule projects. In Part 4, we turn to antibodies and biologics (including anti... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 3: “Small Molecules × AI”) walks through how AI is actually used from hit finding to lead optimization, highlighting concrete use cases, benefits, and limitations.
1. Why Small Molecules Became the Primary Testbed for AI When you scan AI-in-drug-discovery papers and case studies, the first thing you notice is how many of them focus on small molecules. When you scan AI-in-drug-discovery papers and c... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 2: “Data and Algorithms Behind AI-Driven R&D”) explains which data types AI relies on, what sources those data come from, and how representative model families are used in practice.
1. Data and Models: The Core of AI in Drug Discovery In Part 1, we mapped where AI can plug into the drug discovery and development value chain and what it can – and cannot – do. In Part 1, we mapped where AI can plug into the drug disco... -
From Beginner to Expert: AI in Drug Discovery – A Definitive Guide from Lab to Market (Part 1: “What Is AI in Drug Discovery?”) maps the entire R&D-to-market value chain and clarifies what AI can – and still cannot – do.
1. Why AI in Drug Discovery Is in the Spotlight Now “AI in drug discovery” has become a buzzword in recent years, but it is not a magic technology that appeared overnight. “AI in drug discovery” has become a buzzword in recent years, but... -
The Hidden Pitfall of AI Drug Discovery: The Quiet Wall of Reproducibility in Scientific Literature
Introduction: Past Knowledge Is Now the Starting Point of Future Drug Discovery AI-driven drug discovery has become an increasingly central part of modern pharmaceutical R&D. AI-driven drug discovery has become an increasingly centra... -
Using Microbial Signatures the Right Way: QC, Statistics, Deployment, and Atlas Roadmap (Part 2 | For Experts)
Building on Part 1, this expert-oriented post distills practical guidance from the recent TCGA re-analysis and the Genomics England WGS study. The core message is realistic but enabling: robust microbial signatures exist, but in limited ... -
Enteric neuron–gastric cancer organoids expose lipid-metabolic Achilles’ heels — Strengths and limits of organoids for drug discovery, and what comes next
In a nutshell: Genome-wide CRISPR screens in patient-derived gastric cancer organoids uncovered strong dependencies on fatty-acid biosynthesis (ACC/ACACA) and cholesterol biosynthesis (LSS). Co-culture with enteric neurons (ENS) rewires ... -
Can RNA Become a Drug Target? ― Insights from Science and Biotech Frontlines
In recent years, the question of whether RNA can serve as a viable therapeutic target has become one of the most exciting debates in life sciences and drug discovery. In recent years, the question of whether RNA can serve as a viable the... -
Drug Discovery News
BPI Technology Advances Targeted Protein Degradation — A Site-Specific Strategy to Accelerate Drug Discovery Introduction Targeted protein degradation (TPD) has emerged as a transformative strategy in drug discovery, offering a means to ... -
[In vivo CAR T Special #1] Engineering CAR T Cells Directly Inside the Body: Challenges and Opportunities
CAR T-cell therapy has revolutionized blood cancer treatment but traditionally relies on an ex vivo process—extracting T cells from patients, genetically modifying them outside the body, expanding them, and reinfusing them. This manufact... -
[DDN Review] Antibody Combination Approach to Target Allergies at the Source: Drug Discovery News Summary (With My Thoughts)
A U.S. research team is developing a novel combination therapy of antibody drugs that directly targets the underlying causes of allergic diseases. The strategy simultaneously blocks immunoglobulin E (IgE), a key driver of allergic reacti...
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