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ArticleThe future of academic core labs: scaling operational excellence without increasing staff
The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency.
ArticleAI builds dual-action cancer drug targeting PKMYT1
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.
ArticleAI to antibody in days: breaking the wet lab bottleneck via high-throughput integration
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.
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The future of academic core labs: scaling operational excellence without increasing staff
The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency.
AI builds dual-action cancer drug targeting PKMYT1
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 to antibody in days: breaking the wet lab bottleneck via high-throughput integration
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.
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More Artificial Intelligence
The future of academic core labs: scaling operational excellence without increasing staff
The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency.
AI builds dual-action cancer drug targeting PKMYT1
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 to antibody in days: breaking the wet lab bottleneck via high-throughput integration
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.
Beyond serendipity: rational design and AI’s expansion of the undruggable target landscape
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.
AI platform models protein flexibility to accelerate drug design
A new artificial intelligence platform developed at the University of Virginia addresses a critical limitation in computational drug design by modelling protein flexibility during molecular interactions. The suite of tools uses diffusion models to generate drug candidates whilst accounting for induced fit dynamics, potentially improving success rates in early-stage development.
Insilico announces ISM6200 AI-designed drug candidate for ovarian cancer and cortisol disorders
Insilico Medicine has nominated ISM6200, a preclinical drug candidate designed using generative AI to target NR3C1, a receptor involved in cortisol regulation.
Where NAMs stand in early drug discovery: an expert discussion
Register for this webinar to discover the role of NAMs in drug discovery.
Operational data: the hidden driver of faster drug discovery
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.
From fragments to maps: scaling drug–target interaction data
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.
DNA-based system delivers targeted cancer drugs via biomarker logic
Researchers at the University of Geneva have developed a DNA-based drug delivery platform that uses molecular logic gates to identify cancer cells through dual biomarker recognition. The system activates cytotoxic agents only when both tumour markers are present, offering enhanced specificity over current antibody–drug conjugates while enabling deeper tissue penetration and multi-drug combinations.
CRISPR & Genomics: Turning Data into Confident Drug Discovery Decisions
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.
Physics-based modelling offers a new way to study drug targets
Australian start-up OmnigeniQ has demonstrated what it describes as the first deterministic, physics-based computation of a human protein in its native state.
From scientist to bioinformatician: how AI coding tools dissolved the activation energy barrier
A biotech CEO with decades of scientific experience but sporadic coding practice gained practical bioinformatics capabilities in six weeks using AI coding assistants.
New AI-designed T-cell engager LGTX-101 to be presented at AACR in San Diego
LabGenius Therapeutics will present preclinical data for LGTX-101, its AI-designed Nectin-4 x CD3 T-cell engager, at AACR 2026 in San Diego.
ELRIG announces keynote speakers for Advances in Cell-based Screening 2026 in Gothenburg
ELRIG has announced the keynote speakers for its 2026 Advances in Cell-based Screening conference in Gothenburg, where scientists will gather to explore how human-first models, advanced cell biology and AI are changing the future of drug discovery.
Designing targeted assays for clinical success from the start
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.