Drug discovery is generating more data than ever, but the challenge is making sure that data is reliable, connected and usable. At Analytica 2026 in Munich, Drug Target Review spoke with technology developers and industry leaders across the exhibition floor to understand how these challenges are being addressed.
Research published in Nature Communications shows how generative AI can be used to design complex dual-action cancer drug candidates. Insilico Medicine has developed a PKMYT1 degrader that both eliminates the target protein and blocks its activity, demonstrating the growing role of AI in advanced drug discovery.
AI is accelerating drug discovery at an unprecedented pace. Thousands of antibody candidates can now be designed in silico within hours. The challenge now is keeping experimental workflows fast enough to keep up. High-throughput expression and integrated developability assessment are making it possible to move from sequence to data in days.
Promatix Biosciences is developing a new generation of bispecific antibody–drug conjugates using proprietary membrane proteomics data to identify highly selective target pairings. CEO Dr Michael Hunter explains how the company’s TXPro database enables discovery of previously unexplored tumour biology to improve therapeutic index and reduce on-target/off-tumour toxicities in solid tumours.
For decades, drugging the ‘undruggable’ was thought to require luck rather than logic. Today, AI is transforming serendipity into strategy by enabling rational, data-driven approaches to previously inaccessible targets.
The key to faster, smarter drug discovery lies in data that’s often overlooked. By exposing hidden delays and inefficiencies, this data enables teams to shorten discovery cycles and progress promising candidates faster.
Most drug–target data were never designed to be compared at scale. Pharmome mapping takes a different approach, building a shared dataset intended to support more predictable discovery.
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.
Designing gene control from scratch is becoming possible. SynGenSys is using computational design to create synthetic promoters for advanced therapies.
Australian start-up OmnigeniQ has demonstrated what it describes as the first deterministic, physics-based computation of a human protein in its native state.
Neil Bhowmick explores how understanding the mechanisms of cancer drug resistance has reframed our approach to treatment, revealing containment and control as realistic goals for therapeutic strategies.
Research published in Clinical Lymphoma, Myeloma and Leukemia identifies Kappa Myeloma Antigen and Lambda Myeloma Antigen as highly selective immunotherapy targets across plasma cell dyscrasias.
A biotech CEO with decades of scientific experience but sporadic coding practice gained practical bioinformatics capabilities in six weeks using AI coding assistants.
A key player in brain communication and mood regulation, the pharmaceutical industry views the NMDAR as the central pillar for next-generation therapies for depression. Dirk Beher from FundaMental Pharma reveals new strategies for targeting this important receptor.
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.
As IND timelines lengthen, early-stage biotechs face growing uncertainty in early clinical planning. This article explores how sponsors are increasingly diversifying their development strategies and why New Zealand represents an attractive option.
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.