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Beyond the hype: a veteran’s honest assessment of AI in drug discovery – Part 2
Thibault Géoui explains why AI could finally help pharma overcome its productivity crisis and why the payoff won’t come as quickly as the optimists claim.
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Hitchhikers AI and 20/15 Visioneers
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Fixing failed drugs: AI solutions for toxicity in drug discovery – part 1
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 1, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
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Solving the disconnect between lab and data scientists: part 2
As the lab–data science divide continues, Ian Kerman looks ahead to a future of deeper collaboration – one where shared skills, smarter tools and a shift in mindset could finally break down the barriers. In this second interview, he shares his vision, practical ideas and advice for the next generation…
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Solving the disconnect between lab and data scientists: part 1
Lab scientists and data scientists often speak different languages and that miscommunication can slow down important research. In this interview, Ian Kerman shares how his team is working to break down those walls and spark better collaboration.
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Using knowledge graphs in drug discovery (Part 2): how they’re shaping scientific progress
In this second interview of the series, Andreas Kolleger, Head of GenAI Innovation at Neo4j, discusses how knowledge graphs and AI are transforming scientific discovery and improving life sciences workflows.
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Using knowledge graphs in drug discovery (Part 1): how they link to large language models
In this first interview of a two-part series, Andreas Kolleger explores the convergence of knowledge graphs and large language models. As the head of GenAI innovation at Neo4j, Andreas brings a unique cross-industry perspective on how these technologies can enhance life sciences workflows.
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Large language models: now more affordable and reliable than ever
In this article, Dr Raminderpal Singh explores the transformative impact of the Deepseek R1 open-source large language model on drug discovery. Its potential offers exciting opportunities for both scientists and software developers, marking a significant advancement for the life sciences community.
