How AI and cell data combine to make drug discovery faster
Cellarity has published a new paper in detailing an AI-powered framework that integrates single-cell transcriptomics to make drug discovery faster and more successful.
List view / Grid view
Cellarity has published a new paper in detailing an AI-powered framework that integrates single-cell transcriptomics to make drug discovery faster and more successful.
Nature’s pharmacy has yielded half of today’s medicines, yet most of its potential remains untapped. AI is now changing how quickly new therapies can be found.
Within3’s Jason Smith explores how artificial intelligence is breathing new life into next-generation launch situation rooms; delivering actionable insights for pharmaceutical companies.
Part II shows that the predictive validity crisis can be solved by rethinking how the industry chooses models, measures outcomes and integrates systems. Success stories from Vertex, Regeneron and AstraZeneca illustrate how aligning biology, measurement and strategy can reverse decades of declining productivity.
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.
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.
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.
AI has advanced molecule design, yet synthetic feasibility remains a bottleneck. Chemistry-first approaches offer a practical way forward.
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.
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.
Dr Alan Nafiiev evaluates template-based, docking and template-free approaches to PPI prediction, highlighting how AI can enhance structural accuracy.
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.
For decades, molecular glues have been stumbled upon rather than designed. A new scientific approach is now changing that – expanding what is considered druggable.
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.
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.