Novel streamlined screening method could discover new horizons in chemical reaction design
An in-silico reaction screening strategy could produce compounds potentially useful for novel drug development.
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An in-silico reaction screening strategy could produce compounds potentially useful for novel drug development.
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Scientists at the University of Pittsburgh have been using computational modelling to understand the body’s immune responses to avian flu.
The study has highlighted an important advance in using graphene for electrokinetic bio-sample processing and analysis.
Researchers have created a new implant device has the potential to provide an alternative to opioids and other highly addictive drugs.
30 June 2022 | By Brooks Automation
Watch this on-demand webinar to learn how collaborative robots are improving accuracy and efficiency in today’s laboratories.
Researchers used mice to develop a mathematical algorithm of a heart attack.
Dr Richard Goodwin, Head of Imaging & AI, Clinical Pharmacology & Safety Sciences at AstraZeneca, explores the latest innovation in early therapeutic development – spatial biology.
Showcasing select automated instruments and capabilities at Society for Laboratory Automation and Screening Europe 2022 Conference and Exhibition in Dublin, Ireland.
Researchers have implemented structural colour printing in microfluidic fabrication which creates new pathways for medical diagnostics and miniaturised sensors.
New machine learning models could propose new molecules that have specific properties which could fight certain diseases, doing in minutes what might take humans months to achieve manually.
A research centre based at the University of Kansas has been awarded $6.6 million to create lab-on-a-chip technology for medical tests.
Researchers have developed a new bioinformatics pipeline that could help investigate the mechanism underlying the development of autoimmune diseases following SARS-CoV-2 infection.
Sartorius has launched a proprietary fluidic-free instrument platform for real-time, label-free analysis of biomolecular interactions.
Scientists have developed a deep learning-based model trained on drug-induced gene expression signatures to predict drug-drug interactions.