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.
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Researchers have developed an artificial neural network using deep learning to identify genes that are related to disease.
Researchers have used stem cells, CRISPR and gene sequencing technology to create the basis of a new brain cancer model that could offer opportunities for drug discovery.
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Laboratory Information Management Systems (LIMS) is becoming increasingly important in GMP regulated environments in order to ensure data integrity and faster working practices.
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.
A study has shown that unintended mutations from gene editing with CRISPR-Cas9 are rare in zebrafish, providing reassurance that this technology is a valid tool with great promise for the treatment of genetic disorders.
The ICR has revealed that during drug discovery, researchers should not use general search engines and vendor catalogue information to decide on their use of chemical probes.
New software has been developed that can reveal the detailed RNA-binding properties of proteins, which is important for characterising the pathology of many diseases.
Researchers have created an AI model that analyses the citations of studies, predicting their potential for eventual clinical application.
A new platform combines AI, flow chemistry and robotics to minimise the need for human intervention in the synthesis process.
As technology advances and scientific research progresses at a rapid pace, data is being generated in ever-larger quantities. Even with the forward strides in technology and advances in data processing, a common problem widely acknowledged by the scientific community is that of reproducibility. In this article, Matjaz Hren discusses the…
An algorithm has been developed which can predict the outcomes of complex chemical reactions with over 90 percent accuracy which can be applied to drug development.
Researchers hope the system can choose the right patients to enroll in clinical trials, to speed discovery of drug treatments.
Charlotte Walker-Osborn, a Partner and Head of Technology Sector (International) at global law-firm Eversheds Sutherland and a legal expert in technology law, explains some of the challenges and potential future of artificial intelligence (AI) in the field of drug discovery.