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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.
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.
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The 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.
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.
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More Artificial Intelligence
The 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.
From 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.
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.
AI system reduces animal testing in drug discovery by 50 percent
German researchers have developed genESOM, a generative AI system that could reduce animal numbers in preclinical drug testing by 30 to 50 percent.
OpenBind 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.
New 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.
Machine 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.
AI 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.
When 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.
AI 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.
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.