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Advances in informatics have afforded researchers the ability to extrapolate petabytes of human genomics data and translate it into biologically relevant information. However, further translating this information into knowledge can prove challenging. Slavé Petrovski, Vice President and Head of Genome Analytics and Bioinformatics for AstraZeneca’s Centre for Genomics Research, spoke…
The articles in this in-depth focus explain how informatics aids in the development of genomic research and outline the next steps for AI to progress in pharma.
This issue includes a spotlight on how genomic assays could revolutionise healthcare, a discussion on how lab automation can improve drug discovery and an analysis of whether antibodies can provide an effective coronavirus treatment. Other article topics in this issue include immuno-oncology and artificial intelligence.
Using AI and deep learning, researchers have enhanced Scanning Probe Microscopy (SPM) and made their automated resource available for scientists.
Researchers have developed a new screening technique by inactivating the molecular structure of the human muscarinic receptor, allowing them to test potential drugs.
Drug Target Review rounds up the latest updates on research into coronavirus treatments, focusing on virtual screening to find therapies for COVID-19.
The first drug designed using artificial intelligence (AI) has moved into its Phase I trial. Professor Andrew Hopkins of Exscientia explains how an algorithm was used to achieve this milestone.
The newest version of the Unified Data Model (UDM) project has been released, allowing R&D scientists to access information on compound synthesis and testing.
A new antibiotic compound has been identified by researchers who designed a machine-learning algorithm to screen millions of molecules.
Researchers have developed an artificial neural network using deep learning to identify genes that are related to disease.
Research suggests that over half of the total $5.2bn investment for artificial intelligence in pharma went to drug design applications in 2019, but overall investment is slowing.
It is said that, on average, it takes a new drug 12 years to go from research lab to patient, with many thousands of candidates discarded along the way. Can artificial intelligence (AI) help to speed things up? Sheraz Gul and Alp Sahin provide an overview of an AI approach…
A survey has shown that the pharmaceutical industry believes R&D will see a lot of benefit from digital transformation and that AI will be at the forefront.
Researchers have used a machine-learning algorithm to study protein appearance and discover common features that influence function, which could be used to design artificial cells.
This issue includes a discussion on the future of high-throughput screening through collaboration, an analysis of mass spectrometry as a structural biology tool and an exploration of the challenges of hit-to-lead when researching tropical diseases. Also in the issue are articles on immuno-oncology and assays.
