Modelling and simulation software are helping develop new autonomous systems across multiple industries, explains Gemma Church
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Dr Markus Gershater, Co-Founder and Chief Science Officer at Synthace, presents his predictions for the life science industry in the next 12 months
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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Don't miss out on our AWS & TetraScience: 500 Pharma Executives Research Survey. See what pharma executives have to say about the need to replatform scientific data to the cloud.
Planning and implementing a data transformation is not easy, especially in the highly regulated life sciences industry - a deep understanding of the industry, its processes, and regulations is required.
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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We round up the latest laboratory software tools to help streamline laboratory operations in 2023
We round up the latest cluster management software tools available to scientists and researchers using HPC
A round-up of the latest processing and memory technologies
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We round up the latest laboratory software tools to help streamline laboratory operations in 2023
We round up the latest cluster management software tools available to scientists and researchers using HPC
A round-up of the latest processing and memory technologies