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ArticleHow AI is changing the way therapeutic antibodies are designed
What if researchers could design therapeutic antibodies instead of relying on screening millions of candidates? Generative AI is making this increasingly possible, offering new ways to tackle challenging targets.
ArticleVirtual cells vs experimental models: which can you trust?
How reliable are AI virtual cells compared with experimental models? We examine what each can predict or measure, the evidence behind their reliability and their role in early drug discovery.
ArticleScientists are already using AI agents. Most of what they learn goes unpublished.
Dr Raminderpal Singh speaks to Dr Srijit Seal about how AI agents are changing computational drug discovery, the impact on scientists’ daily work and why valuable findings remain unpublished.
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How AI is changing the way therapeutic antibodies are designed
What if researchers could design therapeutic antibodies instead of relying on screening millions of candidates? Generative AI is making this increasingly possible, offering new ways to tackle challenging targets.
Virtual cells vs experimental models: which can you trust?
How reliable are AI virtual cells compared with experimental models? We examine what each can predict or measure, the evidence behind their reliability and their role in early drug discovery.
Scientists are already using AI agents. Most of what they learn goes unpublished.
Dr Raminderpal Singh speaks to Dr Srijit Seal about how AI agents are changing computational drug discovery, the impact on scientists’ daily work and why valuable findings remain unpublished.
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More Artificial Intelligence
How AI is changing the way therapeutic antibodies are designed
What if researchers could design therapeutic antibodies instead of relying on screening millions of candidates? Generative AI is making this increasingly possible, offering new ways to tackle challenging targets.
Virtual cells vs experimental models: which can you trust?
How reliable are AI virtual cells compared with experimental models? We examine what each can predict or measure, the evidence behind their reliability and their role in early drug discovery.
Scientists are already using AI agents. Most of what they learn goes unpublished.
Dr Raminderpal Singh speaks to Dr Srijit Seal about how AI agents are changing computational drug discovery, the impact on scientists’ daily work and why valuable findings remain unpublished.
What will it take to trust human-relevant models in drug discovery?
Could organ-on-a-chip technology help identify drug failures before clinical trials? Jim Corbett of Emulate discusses the evidence needed to make human-relevant models reliable tools in drug discovery.
Finding drug targets through the noise of complex disease
The strongest disease signal may not point to the best drug target. AI could help researchers identify what matters within complex biological networks.
A sceptical guide to AI agents in drug discovery
AI agents promise to transform drug discovery, but where do they really add value? Dr Eric Ma examines search, model choice, vendor lock-in and why scientific expertise matters more than ever.
Drug discovery in Q3 2026 – what changed and why it matters
Agentic AI, ultralarge virtual screening, molecular glues and human-relevant models were among the key developments in early drug discovery during Q3 2026. Drug Target Review examines what changed, why it matters and what to watch heading into Q4.
AI agents vs scientists: what should we automate in drug discovery?
AI agents can search, analyse and plan across drug discovery workflows, but where should scientists hand over control? We examine what to automate, what to check and where human judgement matters most.
Why AI struggles with new drug targets – and how physics could help
Virtual screening can assess billions of compounds, but performance can fall on unfamiliar targets. Combining AI with molecular physics could help make predictions more reliable.
Fast-tracking AI-designed binders with cell-free expression and SPR
AI can rapidly generate new protein binders, but wet-lab validation remains a major bottleneck. Combining cell-free protein synthesis with surface plasmon resonance (SPR) enables AI-designed antibody binders to be screened directly from crude extracts, bypassing lengthy cell culture and purification steps.
Why depression needs a different drug discovery strategy
What if depression is not one disease, but many biologically distinct conditions? A major research programme is investigating what this could mean for biomarkers, drug targets and treatment.
Six assay requirements every researcher using NAMs should know
As NAMs become more widely used in drug discovery, assays must meet the demands of more complex models. Discover six requirements for reliable, reproducible and biologically meaningful data.
Building biomarkers that drug developers can rely on
Discover how integrated technologies, multiomic approaches and AI are helping researchers translate complex biological signals into actionable tools for drug development and patient care.
How AI-driven systems biology will reset the starting line for drug development
AI is making drug discovery faster, but can it make it more successful? Discover why combining AI with systems biology could help researchers tackle the biological complexity behind drug failure.
Why intracellular targets remain a major drug discovery challenge – and how they’re becoming druggable
Many biologically important intracellular targets remain difficult to drug. Dr Rab Prinjha examines how screening within living cells could help tackle them.