All Artificial Intelligence (AI) articles
-
NewsOpenBind launches AI model to accelerate drug discovery
Diamond Light Source has launched OpenBind v1, an open-access AI model and dataset designed to address critical data shortages in drug discovery by providing standardised protein-drug binding measurements at atomic resolution.
-
NewsNew framework evaluates AI reliability in immune system prediction
Researchers at the University of South Florida have developed a comprehensive framework to test how accurately AI systems can predict immune responses, addressing critical questions about the reliability of computational tools in drug discovery.
-
NewsMachine learning identifies five distinct Parkinson’s disease subtypes
A new study from VIB and KU Leuven has revealed that Parkinson’s disease comprises five distinct molecular subtypes, each requiring tailored therapeutic approaches.
-
ArticleThe scale divide: competing strategies in AI drug discovery
Two approaches to AI in preclinical drug discovery are diverging, from multi-thousand GPU systems to models with only a handful of parameters, with early results raising questions about which will deliver.
-
NewsAI tool reveals DNA exists in partially open states
Researchers have discovered that DNA wrapped around nucleosomes exists in multiple partially open states rather than simply locked or accessible configurations.
-
ArticleWhen scale meets insight: reinventing SPR for the future of drug discovery
Carterra’s new 48-channel SPR platform reimagines throughput, automation and data quality for modern discovery workflows.
-
NewsAI system detects pain in laboratory mice through facial analysis
Researchers at ETH Zurich have developed an automated system that uses infrared imaging and artificial intelligence to assess pain in laboratory mice by analysing subtle facial expressions, offering a more consistent and humane alternative to traditional manual observation methods.
-
ArticleFrom leaderboards to lab notebooks: AI designs reach preclinical testing
For years, AI drug discovery has been judged on benchmark performance. Now, a set of studies shows what happens when those designs are made and tested in preclinical settings.
-
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.
-
ArticleBeyond 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.
-
NewsAI 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.
-
NewsInsilico 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.
-
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.
-
WebinarWhere NAMs stand in early drug discovery: an expert discussion
Register for this webinar to discover the role of NAMs in 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.
-
ArticleOperational 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.
-
ArticleFrom 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.
-
NewsDNA-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.
-
ReportCRISPR & 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.
-
InterviewPhysics-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.