Discover how workflows are being accelerated to speed up the vaccine research and development process while maintaining safety and immunogenicity.
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Computational drug screening has shown that chemotherapy drug pralatrexate could potentially be repurposed to treat COVID-19.
Dr Isaac Karimi and his team explain how compounds to treat COVID-19 could be found in Kurdish ethnomedicine, selecting some plants for computational drug discovery.
Scientists have trained a computer algorithm to identify differences in cancer cell lines based on microscopic images, preventing the misclassification of cells.
Dr Diogo Camacho from the Wyss Institute at Harvard discusses new research into using machine learning algorithms to analyse RNA sequences and reveal potential drug targets.
Researchers at Ingenza and the University of Plymouth are using a machine learning and high‑throughput screening approach to discover novel antimicrobial therapies. In this article, Nikki Withers spoke to one of the researchers, Jack Kay, to hear about the current threat posed by antimicrobial resistance and how he hopes their…
An artificial intelligence (AI) system called AlphaFold has been developed to effectively predict protein structures and folding.
A model of a human lung cell has been used to understand how SARS-CoV-2 uses host cell processes to reproduce, revealing drug targets.
In this journal, find articles discussing antimicrobial resistance, exploring why inhibiting the interaction between SARS-CoV-2 and neuropilin-1 could help combat COVID-19, as well as how CRISPR can be used to enhance productivity in cell line development. Also in this issue, features on engineering new biologic drugs and precision medicine.
UKRI will provide £4 million in funding to establish a data infrastructure for scientists in the UK to study antibodies from COVID-19 patient samples.
The tool uses interactive molecular dynamics simulations in virtual reality (iMD-VR) to allow researchers to step inside SARS-CoV-2 enzymes and visualise molecules binding to them.
The semi-automated process enabled researchers to make retinal organoid production and selection nearly four times faster.
The Automated Recommendation Tool (ART) uses machine learning to accelerate the development of cells for specified goals, eg, bioprocessing and bioproduction.