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ArticleWhy 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.
ArticleReducing drug development failures with human-relevant models and AI
Professor Joseph C. Wu of Stanford University explains how stem cells, human-relevant models and AI are helping researchers predict which drug candidates are most likely to succeed before clinical trials.
ArticleWhy single-cell foundation models have underdelivered and what drug discovery needs instead
Foundation models have delivered breakthroughs in protein biology, but single-cell models have struggled to match them. What is holding them back?
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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.
Reducing drug development failures with human-relevant models and AI
Professor Joseph C. Wu of Stanford University explains how stem cells, human-relevant models and AI are helping researchers predict which drug candidates are most likely to succeed before clinical trials.
Why single-cell foundation models have underdelivered and what drug discovery needs instead
Foundation models have delivered breakthroughs in protein biology, but single-cell models have struggled to match them. What is holding them back?
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More Artificial Intelligence
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.
Reducing drug development failures with human-relevant models and AI
Professor Joseph C. Wu of Stanford University explains how stem cells, human-relevant models and AI are helping researchers predict which drug candidates are most likely to succeed before clinical trials.
Why single-cell foundation models have underdelivered and what drug discovery needs instead
Foundation models have delivered breakthroughs in protein biology, but single-cell models have struggled to match them. What is holding them back?
AI’s promise and practical limits in drug discovery
From ChatGPT to AI agents and world models, where is AI really heading in drug discovery? Dr Raminderpal Singh explains what researchers should focus on now.
Making historical toxicology data work harder in safety assessment
Historical toxicology data is often underused. Find out how Virtual Control Groups and AI could help researchers strengthen safety assessment while making better use of existing data.
How AI and human biology data led to a new addiction drug candidate
How can human biology data improve target selection? Learn how one discovery programme identified a potential new treatment for opioid use disorder.
AI’s real value in drug discovery may be choosing the right experiment
AI is becoming more capable, but its value still depends on the data, questions and decisions behind it. Where is it genuinely improving drug discovery and where do the limitations remain?
Drug development has changed. Bioanalysis is changing with it
As drug developers pursue increasingly complex therapies, traditional bioanalytical approaches are being put to the test. How is the field adapting to meet these new demands?
Context is everything: how spatial biology is changing our understanding of disease
Discover how spatial biology is revealing disease mechanisms with implications for biomarkers, immunotherapy and drug development.
AACR 2026 part two: integrating AI, spatial biology and next-gen therapeutics
In part two of our AACR 2026 coverage, industry leaders were focussed on how the field is no longer constrained by data generation or molecular design, but by the challenge of connecting systems, standardising workflows and ensuring biological insights.
AI in Drug Discovery: Progress, Limits and What Comes Next
AI has attracted enormous investment across drug discovery, but major questions still remain around validation, reproducibility and real-world application. In our latest Beyond the Lab report, experts discuss where the technology is starting to influence discovery workflows – and where limitations continue to slow adoption.
From chemist to AI agent builder: inside the rise of agentic AI in drug discovery
Dr Raminderpal Singh speaks with Dr Srijit Seal about why specialised AI agents are outperforming general-purpose models in drug discovery and what a new consortium paper shows about their use in practice.
Machine learning identifies biological signals linked to emotional hunger
Researchers at Phenomix Sciences are using machine learning and genetic risk scoring to investigate emotional hunger, an obesity phenotype linked to emotional and reward-driven eating behaviours. Dr Timothy O’Connor discusses how the approach could improve patient stratification, obesity research and treatment selection.
Why AI models need patient data to deliver in drug discovery
Despite rapid advances in AI, many drug discovery models still struggle to translate computational predictions into clinical outcomes. Thomas Clozel explains how Owkin is training AI on large-scale patient-derived data while integrating experimental and clinical validation directly into model development.
Turning GWAS signals into drug targets with scalable CRISPR
Genome-wide association studies have linked thousands of genetic variants to disease, yet most remain disconnected from drug-relevant biology. Neville Sanjana, Professor at New York University and Core Faculty Member at the New York Genome Center, explains how scalable CRISPR screens systematically link noncoding variants to causal genes and therapeutic targets.
AACR 2026 part one: AI design, precision biology and the next wave of oncology innovation
At AACR 2026, industry leaders discussed how oncology R&D is moving beyond isolated technological advances towards integrated discovery systems.