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ArticleAI’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?
ArticleDrug 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?
NewsEuropean initiative aims to bring molecular dynamics into mainstream drug discovery
What if the vast amounts of data generated by molecular dynamics simulations could be routinely shared and reused? A new €10 million European initiative aims to do just that, helping researchers gain a deeper understanding of protein behaviour and drug-target interactions.
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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?
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.
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More Artificial Intelligence
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?
European initiative aims to bring molecular dynamics into mainstream drug discovery
What if the vast amounts of data generated by molecular dynamics simulations could be routinely shared and reused? A new €10 million European initiative aims to do just that, helping researchers gain a deeper understanding of protein behaviour and drug-target interactions.
Context is everything: how spatial biology is changing our understanding of disease
Studying individual cells has revolutionised biomedical research, but it doesn’t tell the whole story. 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.
When scale meets insight: reinventing SPR for the future of drug discovery
Carterra’s new 48-channel SPR platform reimagines throughput, automation and data quality for modern discovery workflows.
From leaderboards to lab notebooks: AI designs reach preclinical testing
For years, AI drug discovery has been judged on benchmark performance. Now, a set of studies shows what happens when those designs are made and tested in preclinical settings.
The future of academic core labs: scaling operational excellence without increasing staff
The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency.
AI builds dual-action cancer drug targeting PKMYT1
Research published in Nature Communications shows how generative AI can be used to design complex dual-action cancer drug candidates. Insilico Medicine has developed a PKMYT1 degrader that both eliminates the target protein and blocks its activity, demonstrating the growing role of AI in advanced drug discovery.
AI to antibody in days: breaking the wet lab bottleneck via high-throughput integration
AI is accelerating drug discovery at an unprecedented pace. Thousands of antibody candidates can now be designed in silico within hours. The challenge now is keeping experimental workflows fast enough to keep up. High-throughput expression and integrated developability assessment are making it possible to move from sequence to data in days.