For decades, bringing a drug from concept to patient access has been an expensive, convoluted and often uncertain journey. The traditional drug discovery process relies heavily on trial and error, biological hypotheses and rigid scientific processes. Various challenges significantly prolong the drug discovery process, ultimately slowing the pace of innovation and delivery of treatment for patients. Today, the integration of artificial intelligence into the drug discovery proc

Retrosynthesis is the chemist’s toolkit for building complex molecules from simpler ones. Once rooted in intuition and experience, the process has been transformed by artificial intelligence and big data. This article explores how retrosynthesis works, why it matters and how it’s shaping the future of chemistry – from drug discovery to materials science

What’s the evolving impact of digitalisation on life sciences operations, and how can a systems-engineered approach help to create more resilient, flexible and future-ready compliant thermal processes?

From accelerating drug discovery to enabling earlier cancer diagnosis, how is artificial intelligence redefining value creation, transforming patient care and prompting a shift from reactive treatment to proactive intervention?

James Kugler at EMD Digital/Merck speaks to IPT about how counterfeit drugs, regulatory pressures and supply chain vulnerabilities demand a greater level of transparency in pharmaceutical manufacturing. Cyber-physical trust platforms, which provide an immutable link between physical products and their digital twins, will set a new standard and revolutionise the industry

How will advancements in technology affect the pharma sector, from both commercial and R&D points of view?

Digital tools, such as artificial intelligence, are improving pharma drug discovery

Cloud-based tools like process analytical technology can be used to improve pharma manufacturing processes, speeding up quality assurance and time-to-market in a cost-effective manner

The use of artificial intelligence is becoming increasingly common in life sciences R&D. In what areas can it be utilised in the most helpful manner?

How is generative AI being utilised in drug discovery to improve processes and secure higher-quality drugs?

The world is rapidly changing with numerous and frequent digital transformations. How can these be implemented and utilised effectively to ensure customer-centricity in the pharma sector?

AI, especially genAI, is becoming increasingly popular in the pharma and life sciences industries. How can companies use large language models – and more specifically, retrieval augmented generation architectures – to ensure AI-generated results are accurate?