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As AI reshapes scientific work, two founders debate how best to build tools scientists can trust — should we embed expertise into the model or the team? From agent-powered labs to hypothesis-generating machines, the future of drug discovery is being reimagined right now.
AI is reshaping drug discovery – but not without resistance. In this two-part conversation, André França and Eli Pollock share honest insights about the real barriers to AI adoption in life sciences and how embedding domain expertise into AI workflows might be the key to unlocking its full potential.
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.
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.
In this second part of a two-part series, we continue Sujeegar Jeevanandam’s exploration of the future of AI in drug discovery. We share his vision for transformative AI applications, such as simulating human pharmacokinetics and pharmacodynamics, and offer strategic recommendations for biotechs looking to adopt AI.
Download our latest report to discover how AI is transforming drug discovery, accelerating treatments and driving personalised care.
Insilico Medicine’s AI-designed drug for idiopathic pulmonary fibrosis (IPF), Rentosertib, has been granted an official name by USAN. This is the first drug where both the target and compound were discovered using generative AI, marking a major milestone in AI-driven drug development.
AI is transforming drug discovery, but its adoption mirrors past technological shifts in the industry. In this first part of a two-part series, we reveal Sujeegar Jeevanandam’s observations of the parallels between AI and the electronic lab notebook revolution, highlighting key challenges, lessons learned, and what the future holds for…
Google has introduced an AI-powered 'co-scientist' designed to accelerate biomedical research and drug discovery by generating scientific hypotheses and identifying novel therapeutic targets.
Scientists have developed an AI algorithm capable of searching through 10 sextillion potential drug molecules, a feat previously considered impossible. This method could significantly speed up drug discovery and the development of new treatments.
Insilico Medicine’s AI-designed CDK12/13 inhibitors show promise against treatment-resistant cancers. Find out how this breakthrough could reshape cancer therapy.
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.
Researchers at the University of Virginia School of Medicine have developed computer models to create more targeted antibiotics. This approach aims to fight antibiotic resistance by focusing on specific bacteria in different parts of the body, reducing the reliance on broad-spectrum antibiotics.
Macrocycles outperform traditional therapeutics on several fronts, yet they account for just four percent of FDA-approved drugs. Colleagues Ann E Cleves and Ajay N Jain from Optibrium Ltd explain why we can expect to see this number increase in the coming years.
MIT's new open-source AI model, Boltz-1, could transform drug discovery by accurately predicting protein structures, offering global access to researchers.
