Despite the buzz around artificial intelligence (AI), most industry insiders know that the use of machine learning (ML) in drug discovery is nothing new. For more than a decade, researchers have used computational techniques for many purposes, such as finding hits, modelling drug-protein interactions, and predicting reaction rates.
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Some 50-80% of research scientists' and data scientists' time is spent wrestling with data before they can focus on higher value AI/ML and advanced analysis to help bring new life-saving therapeutics to market.
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From new instruments and software to the rise of the digital lab, the landscape of analytical chemistry data continues to evolve, writes Sanji Bhal, Director, Marketing & Communications
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Despite the buzz around artificial intelligence (AI), most industry insiders know that the use of machine learning (ML) in drug discovery is nothing new. For more than a decade, researchers have used computational techniques for many purposes, such as finding hits, modelling drug-protein interactions, and predicting reaction rates.
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Laboratory informatics tools have continued to converge around requirements for data management and movement within an organisation.
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A round-up of the latest technologies available to scientists and researchers using HPC
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A round-up of the latest processing and memory technologies
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Laboratory informatics tools have continued to converge around requirements for data management and movement within an organisation.
Credit: Larichshutterstock/Shutterstock
A round-up of the latest technologies available to scientists and researchers using HPC
Credit: Carlos Castilla/Shutterstock
A round-up of the latest processing and memory technologies
