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Natural products play an underappreciated role in drug discovery. Tandem mass spectrometry (MS/MS)-based metabolomics coupled with machine learning is allowing new, highly diverse molecules from natural products to be identified, revealing bioactive compounds and pinpointing promising drug targets. The additional dimension provided by trapped ion mobility (TIMS) enables researchers to…
In this Q&A, Dr Gerard Wong elucidates the inflammatory capacity of fragmented viral components from the perspective of supramolecular self-organisation.
Researchers conducted a proteogenomic characterisation and found that drug exposure changes drug sensitivity.
The computer model enables a better understanding of how drugs affect fibroblasts and finds a promising candidate to prevent heart scarring.
The ML algorithm explores how genetic mutations collectively influence a tumour’s reaction to drugs impeding DNA replication.
Dr Ketan Patel, Clarivate, shares his insights about the use of Real-World Data and genomic biomarker data and discusses how researchers can use these to better detect and diagnose diseases.
Disruptions in TP53 and RB1 are key influencers that cause changes in the risk of mutations across chromosomes.
Data from protein analyses, combined with data from patient journals, enabled the discovery of proteins that predict disease progression.
Novel approaches to brain disorder diagnosis and treatment may be developed following a project to form a map of the mouse hippocampus.
A certain macrophage phenotype is more effective than another phenotype commonly used in cell therapy for infiltrating tumours.
Tune into this podcast to understand why researchers are turning to 3D Organoids and how organoids can be made ready for screening.
Artificial intelligence (AI) and machine learning (ML) have been gaining significant attention lately, primarily in discussions about their responsible utilisation. However, these technologies possess a wide spectrum of practical applications, ranging from predicting natural disasters to addressing social disparities. Now, AI is making its mark in the field of cancer…
Rob Scoffin and Matthew Habgood from solutions provider Cresset look to the future of drug discovery and the roles that artificial intelligence and machine learning could play.
Machine learning (ML) presents a promising opportunity to revolutionize early cancer detection in primary care, addressing the challenges associated with diagnostic errors and improving patient outcomes. The potential of ML in this field is highlighted in a recent paper published in Oncoscience
Cornell University launches $11.3 Million Scientific Artificial Intelligence Centre to unlock the potential of human-AI collaboration in scientific discoveries.
