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How real-world data is accelerating drug discovery
Vish Srivastava considers the benefits of expanding the role of real-world data in drug discovery to provide improved therapies, faster and with greater success.
List view / Grid view
Informatics
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Building reliable organoid models for human-relevant drug discovery
Organoids are changing the landscape of biomedical research, with automation and AI driving new levels of consistency, scalability and human relevance. Aaron Risinger of Molecular Devices discusses how these technologies are advancing precision medicine – and the challenges that remain.
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Meta-Flux expands AI platform to speed up drug development
Dublin-based biotech Meta-Flux has raised €1.8M ($2M) in seed funding to expand its AI-driven platform for preclinical drug development, helping researchers predict drug success and accelerate the pathway from lab to clinic.
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Chemistry-aware AI offers new routes in small molecule design
AI has advanced molecule design, yet synthetic feasibility remains a bottleneck. Chemistry-first approaches offer a practical way forward.
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The predictive validity crisis: Pharma’s productivity paradox – Part I
Drug discovery now costs 100 times more per FDA-approved drug than in 1950, despite vast advances in biology and computing. The core problem is the collapse of predictive validity in preclinical models, which sits at the heart of pharma’s productivity paradox.
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Eye movements as objective biomarkers: accelerating CNS drug development
Measuring disease progression remains one of the biggest hurdles in CNS drug development. Eye movements, now trackable with just a laptop and webcam, are emerging as a sensitive and scalable biomarker that could transform how trials are designed and therapies reach patients.
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How smoking and alcohol shape mutations in our DNA
Researchers have refined a cutting-edge DNA sequencing tool that reveals how mutations accumulate in healthy tissues as we age, offering insights into the earliest stages of cancer development.
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CloneSeq-SV: new blood test tracks ovarian cancer recurrence
Researchers have developed a new blood test method, CloneSeq-SV, that tracks treatment-resistant ovarian cancer cells over time. The approach could help predict recurrence and guide targeted therapies.
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Beyond templates: advancing protein–protein interaction structure prediction with AI
Dr Alan Nafiiev evaluates template-based, docking and template-free approaches to PPI prediction, highlighting how AI can enhance structural accuracy.
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From data to therapy: emerging tech driving cancer drug discovery
Multiomics, AI and liquid biopsies are giving researchers real-time insight into tumour biology and enabling more personalised cancer therapies. Find out how these technologies are advancing biomarker discovery, improving patient stratification, and guiding the design of new treatments.
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Making sense of AI: bias, trust and transparency in pharma R&D
AI is increasingly used in drug discovery, but hidden bias and ‘black box’ models threaten trust and transparency. This article explores how explainable AI can turn opaque predictions into clear, accountable insights.
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AI meets human tissue to fast-track precision medicine development
By combining human tissue models with explainable AI, researchers can analyse complex patient data to identify which treatments work best for which patients. First applied to inflammatory bowel disease, this approach could improve clinical trial success rates across many diseases.
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Lab of the future: four technologies to watch
From precision proteomics to AI-powered immune profiling, next-generation laboratory technologies are changing how new therapies are discovered and developed. Here are four innovations set to shape the lab of the future - and the future of drug discovery.
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Animal-free drug discovery is closer with QSP
Quantitative Systems Pharmacology (QSP) is fast becoming a standard tool in drug development, offering a human-relevant way to predict drug effects before the clinic. Dr Josh Apgar of Certara explains how it is helping to cut reliance on animal testing and speed discovery.
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New AI method maps how tuberculosis drugs destroy bacteria
Scientists at Tufts University have developed an AI tool that demonstrates how tuberculosis drugs kill bacteria – an advancement that could speed-up the discovery of shorter, more effective treatments.
