Early drug discovery has no shortage of genomic data, but confidence remains scarce. This report examines how CRISPR, functional genomics and human-relevant models are being applied to determine which signals matter, how they influence disease biology and which targets and strategies are worth pursuing.
SynGenSys applies computational design strategies to engineer synthetic promoters with predictable performance characteristics for therapeutic and manufacturing applications. Professor David James discusses how tissue-specific regulatory elements are designed from genomic data to enable precise control of gene expression in contexts ranging from NK cell immunotherapy to biologic production.
Australian start-up OmnigeniQ has demonstrated what it describes as the first deterministic, physics-based computation of a human protein in its native state.
A biotech CEO with decades of scientific experience but sporadic coding practice gained practical bioinformatics capabilities in six weeks using AI coding assistants.
Why do some targeted assays move smoothly from discovery to clinical practice while others stall? The answer often lies in the earliest design decisions, where choices about samples, platforms and data determine what is possible later.
Automation is helping drug discovery teams screen faster, cut costs and run complex assays at scale – but its real value lies in what happens next.
At World ADC London 2026, experts highlighted how advances in payload design, targeting strategies and AI-driven discovery are changing antibody–drug conjugate development.
Automation and artificial intelligence are changing how scientists design, test and refine new molecules. At the University of Toronto, Stuart R Green and the Acceleration Consortium are building a self-driving lab that could change the pace of early drug discovery.
For International Women’s Day, Dr Amanda Hemmerich, Global Director of Digital Pathology & Innovation at IQVIA Laboratories, describes how digital pathology is being applied in early drug development and what it takes to build credibility in a multidisciplinary technical field.
To study the biological underpinnings of autism, researchers must examine the human brain itself. This article explores how Autism BrainNet supports this work through coordinated tissue donation and preservation.
Most labs want to use AI, but few have the digital foundations to support it. Cenevo’s leaders explain why progress is slow and what laboratories must fix before AI can deliver real value.
Experts from the World ADC Conference in London highlight how patient-centric, predictive preclinical tools and innovative ADC designs are improving safety, efficacy and clinical translation.
Drug discovery has no shortage of powerful technologies, but the challenge now is making them work together. At SLAS Boston 2026, researchers and technology developers revealed how laboratories are connecting the entire experimental pipeline.
Immunotherapies such as CAR-T are extending survival, yet reliance on inpatient monitoring for cytokine release syndrome continues to restrict access. This article explores how continuous digital monitoring and AI-driven analysis could enable safer outpatient delivery and support more scalable immunotherapy adoption.
Lukas Gaats and his team at mo:re are using automation to bring consistency to 3D cell culture and move drug discovery beyond animal models. Read on to find out how.
In drug discovery, great science alone is not enough because commercial viability ultimately decides which programmes survive and attract partners. This Q&A explores how integrating Business Development and Licensing (BD&L) from the earliest stages can guide R&D strategy, sharpen decisions and de-risk the path to market.
The UK has set out a strategy to replace animal testing, but delivering change will depend as much on regulation as on technology. Dr Emma Grange, Director of Science and Regulatory Affairs at Cruelty Free International, examines what the policy signals for research, drug discovery and safety assessment.
Find out how AI-assisted development is democratising software creation in life sciences.
As AI drug discovery enters 2026, the industry faces a pivotal year of clinical tests, regulatory clarity, and market consolidation. Here, Dr Raminderpal Singh examines where AI is delivering measurable gains in early discovery, where hype outpaces reality and why Phase III results will determine whether the technology can truly ...
Artificial intelligence is increasingly used to analyse large, multimodal Alzheimer’s datasets and inform target discovery and trial design. A new JPAD special issue highlights how these methods are moving from experimentation towards practical application in drug discovery.