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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.
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Artificial Intellingence
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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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Scientists track amyloid plaques in living mice for first time
A new fibre-optic method lets researchers monitor amyloid plaque buildup in living, freely moving mice – offering a minimally invasive way to track Alzheimer’s disease progression and test potential therapies.
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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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Inside Zasocitinib: a new model for TYK2 inhibition in immune-mediated diseases
Zasocitinib is a highly selective, investigational TYK2 inhibitor developed to target immune-mediated diseases with fewer off-target effects than traditional JAK inhibitors. This article explores its mechanism, selectivity data and clinical progress.
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The science behind the systematic discovery of molecular glues
For decades, molecular glues have been stumbled upon rather than designed. A new scientific approach is now changing that – expanding what is considered druggable.
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Fibroblast mapping reveals potential universal drug targets
Scientists have mapped the diversity of fibroblasts and discovered how ‘rogue’ fibroblasts drive multiple diseases, revealing drug targets that could transform treatments across the body.
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Registration for ELRIG’s Drug Discovery 2025 closes on 30 September
Registration for ELRIG’s Drug Discovery 2025 will close on 30 September. The free to attend conference, held on 21–22 October in Liverpool, will bring together thousands of scientists, exhibitors and expert speakers.
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ProRNA3D-single: new AI for drug discovery and disease research
Virginia Tech computer scientists have created a new AI tool, ProRNA3D-single, that can generate 3D models of how viral RNA binds to human proteins – a development that could speed up drug discovery.
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Mapping lung disease: new atlas to accelerate drug discovery
Helmholtz Munich and Parse Biosciences have collaborated to create the world’s largest lung disease perturbation atlas – which could aid the discovery of new therapeutic targets and accelerate the development of future lung disease 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.
