Pharma R&D Today

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Finding the Corpus of Knowledge for Machine Learning/AI In Chemistry

Posted on January 14th, 2020 by in Chemistry

Machine learning for predicting chemistry is an area of intense research and publication. However, since the terminology used to describe this activity is diverse it can be difficult to identify all of the publications describing use of computers to predict chemical outcomes or retrosynthesis paths.

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What’s ahead for the Life Sciences industry?

Posted on December 30th, 2019 by in Pharma R&D

As the year comes to a close, we’re thinking about trends in the landscape and where the industry is headed. What do some of the members of Elsevier’s Life Sciences team think?

Tom Pianko VP of Global Key Accounts, counts machine learning, artificial intelligence, data normalization and advance analytics tools among the trends that point to where the Life Sciences industry is going—and many of his colleagues agree.

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What were the most important lessons in Life Sciences in 2019?

Posted on December 20th, 2019 by in Pharma R&D

As 2019 comes to a close, we asked members of Elsevier’s Life Sciences team:

What were the biggest lessons or most noteworthy developments in the Life Sciences industry this year?

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Elsevier uses machine learning to benefit pharmacovigilance

Posted on November 7th, 2019 by in Pharmacovigilance

Monitoring the scientific literature for adverse drug reactions (ADRs) is critical to maintaining drug safety, and there is no room for error. As regulations tighten, pharmacovigilance teams are seeking better strategies and methods for ensuring that all ADRs are identified in the most effective and efficient way possible.

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