Adapting drug discovery to Artificial Intelligence

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Posted: 25 July 2018 Ami S Lakdawala (GSK's In-silico drug discovery unit), George Okafo (GSK's In-silico drug discovery unit), John Baldoni (GSK's In-silico drug discovery unit), Michael Palovich (GSK's In-silico drug discovery unit), Tobias Sikosek (GSK's In-silico drug discovery unit), Voshal Sahni (GSK's In-silico drug discovery unit) No comments yet Drug discovery has always been challenging; today, more so than ever. While there has been success in addressing many diseases, others remain intractable. There is a need and opportunity to explore new drug discovery approaches that harness immense datasets (public and private), which have been built upon the successes and failures of the past to guide in-silico approaches to new therapies. Advances in genetics and molecular biology have revealed potential new targets for developing medicines. Deciding which target to pursue is challenging and an area in which there is opportunity to increase productivity.


Machine learning poised to accelerate drug discovery Novartis

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A technology called machine learning is behind that seemingly magical ability of social networking websites to identify people in posted photos. By analyzing subtle patterns in facial features, machine learning algorithms can recognize people you've never tagged and might not even know. Machine learning is also transforming how scientists at Novartis discover and develop new drugs. Similar to how social networking websites use the technology to classify people on your computer screen, Novartis scientists use it to classify digital images of cells, each treated with different experimental compounds. Biological insights that might take months to generate using time-consuming laboratory experiments and human visual inspection can be revealed much faster using automated computer algorithms looking at pictures.


Machine learning poised to accelerate drug discovery Novartis

#artificialintelligence

A technology called machine learning is behind that seemingly magical ability of social networking websites to identify people in posted photos. By analyzing subtle patterns in facial features, machine learning algorithms can recognize people you've never tagged and might not even know. Machine learning is also transforming how scientists at Novartis discover and develop new drugs. Similar to how social networking websites use the technology to classify people on your computer screen, Novartis scientists use it to classify digital images of cells, each treated with different experimental compounds. Biological insights that might take months to generate using time-consuming laboratory experiments and human visual inspection can be revealed much faster using automated computer algorithms looking at pictures.


Knowledge Discovery in Databases: An Overview

AI Magazine

After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book Knowledge Discovery in Databases were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to "AI Magazine readers of this article.


AI researcher argues machine learning discoveries require checking

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The accuracy and reproducibility of scientific discoveries made with machine-learning techniques should be questioned by scientists until systems can be developed that effectively critique themselves, according to a researcher from Rice University. Allen says that it appears that discoveries currently being made by applying machine learning to large data sets can probably not be trusted without confirmation, "but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Developing predictive models has been one of the focuses of the ML field, according to Allen. "A lot of these techniques are designed to always make a prediction," she notes. "They never come back with'I don't know,' or'I didn't discover anything,' because they aren't made to."