Pharma R&D Today

Ideas and Insight supporting all stages of Drug Discovery & Development

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Interview with Greg Landrum at Elsevier: What are the ingredients of a successful Open Source cheminformatics software?

Posted on November 30th, 2020 by in Chemistry

(Written by Elena Herzog in collaboration with Markus Fischer, Gerd Blanke, Jarek Tomczac and Gabrielle Whittick)

RDKit, a collection of cheminformatics and machine learning software, is assisting in solving chemical information challenges. The founder and creator of RDKit, Greg Landrum, was interviewed by the UDM (Unified Data Model) team, facilitated by Elsevier, to share his experience on what the road to success looks like and what ingredients does an open source project need to have to be successful. The learnings from the interview would help to shape the future of the UDM project, which is transferring from its consortium-led Pistoia Alliance model to a community-led model.

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Infographic: Elsevier’s Professional Services team provides COVID-19 insights

Posted on May 1st, 2020 by in COVID-19

Check out a special infographic we created to show how Elsevier’s Professional Services team can support your COVID-19 research.

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Five drug development strategies to combat 2019 novel coronavirus

Posted on April 15th, 2020 by in COVID-19

This article was originally published on Feb 28, 2020, and has been updated on Apr 13, 2020.

Coronavirus disease 19 (COVID-19) has rapidly spread from its starting point in Wuhan, China. As of February 23, 2020, a total of 32 countries and territories have reported 78,811 cases (98% in China) and 2462 deaths [1]. The international scientific community is in a race against time. Researchers across the globe are collaborating to develop antivirals and vaccines to contain the spread of 2019 novel coronavirus (SARS-CoV-2).

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AI in Drug Discovery Has Great Potential – But Also Significant Barriers

Posted on May 1st, 2019 by in Pharma R&D

Elsevier’s Tim Miller’s article in World Pharma Today provides a insightful lens into the reality behind the complexities of implementing artificial intelligence into the R&D cycle. While great strides have been made, the runway to realize the potential is a long one and there are many exciting discoveries left to be made and obstacles to overcome.

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