AI Drug Discovery– category –
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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. At the same t... -
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: Small molecules × AI Antibodies, bispecifics, and ADCs × AI Nucleic acid and RNA therapeutics × AI Cell and gene therapies × AI Each time, we looked a... -
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 hypothesis generation and prioritization, not as an “answer machine.” This Expert Edition g... -
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. In these modalities, the design space is... -
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 4, we turn to antibodies and biologics (including antibodies, bispecifics, antibody–drug conjugates, and... -
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. This is not just because small-molecule R&D... -
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 2, we zoom in on the foundations: data and models.... -
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. Traditional in silico approaches – QSAR, docking, and statistical... -
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. The speed at which novel drug candidates can be identified... -
[AI Drug Discovery: Reality #1] How Far Has AI Drug Discovery Progressed? Sorting Hype from Reality
With the rapid evolution of artificial intelligence (AI), the concept of AI Drug Discovery has gained significant attention. Headlines often suggest that AI can now "create new drugs" or "automate drug development," raising expectations ...
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