Pharma R&D Today

Ideas and Insight supporting all stages of Drug Discovery & Development

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Can predictive retrosynthesis become a valuable part of a chemist’s toolkit?

Posted on January 18th, 2022 by

Chemistry

“Yes. I was amazed by the Reaxys Predictive Retrosynthesis technology,” says Moritz Classen of the Carreira Research Group at ETH Zurich. He and his colleagues spent months evaluating the tool as they applied it into their daily workflows. “It’s already very user-friendly and really does save a lot of time”. So, what can chemists exactly expect when using predictive retrosynthesis tools?

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The power of similarities to accelerate antiviral development for COVID-19

Posted on January 11th, 2022 by

COVID-19

In April 2020, I wrote a blog post entitled “Exploiting similarities between SARS-CoV-2 and other viruses to accelerate vaccine and therapy development,” where I described research areas leveraging genetic and phenotypic similarities between SARS-CoV-2 and other known viruses. In my quest to find relevant data, I used Reaxys Medicinal Chemistry to generate a list of substances that interact with enterovirus 3C proteases, a target showing striking similarities with the main proteases of SARS-CoV and SARS-CoV-2. A set of interesting derivatives using a cyanohydrin as an anchor group caught my attention.1 Interestingly, Pfizer used the cyano warhead a few months later to design their protease inhibitor (PF-07321332). Pfizer’s antiviral forms a complex structure with SARS-CoV-2 Mpro, demonstrating the efficacy of the cyano warhead to form a covalent bond with the cysteine residue at the active site.2

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AI-driven innovation in life sciences: Unlocking the page – 20+ years of digital innovation at Elsevier

Posted on December 2nd, 2021 by

AI & Data

Mark Sheehan is VP Data Science on Elsevier’s Life Science team. His 22 years at the company maps closely with Elsevier’s digital journey over that time. And today, pharmaceutical companies can follow a similar journey – albeit highly accelerated – using Elsevier’s latest AI-driven R&D boosters. Mark has enjoyed many valuable experiences along the way, from the joys of cracking open a newly printed book, to enabling people to speedily crack new synthetic pathways at scale. “Yes, innovation always involves new technology. But it’s equally about human collaboration,” he says.

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AI-driven innovation in life sciences: Data as data should be

Posted on November 17th, 2021 by

AI & Data

Your AI applications are only as strong as your data. This is why Elsevier strives to make data not only align with FAIR usability principles but also be maximized for trustworthiness and relevance. In this way we can confidently offer the full potential of data science in an easy-to-use manner – from data acquisition and preparation, to AI application and interpretation.

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Big Data in Chemistry is featured in a new webinar series

Posted on November 11th, 2021 by

Chemistry

We talk a lot about how innovation is driven by technological advances such as AI and machine learning – but equally important to innovation are partnerships and collaborations. The R&D Collaboration Network, founded in 2015, brings together top-class researchers in chemical informatics and computational chemistry who need access to comprehensive, accurate, diverse and deep sources of chemical data as input to their research programs, in order to drive innovation via the development of visual and/or predictive analytics approaches.

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AI-driven innovation in Life Sciences: Data Science & AI amplifies your innovative drug R&D capabilities

Posted on November 4th, 2021 by

AI & Data

If you work in Life Sciences & Health, you’re likely aware of what Data Science and AI can bring to the table when it comes to sparking innovation. However, if you are not Big Pharma, it’s the how that slows down most LSH companies and organizations. We believe that Elsevier’s road-tested data and semantic technologies can address the challenges of Life Science data.

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About this Blog

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The Elsevier Pharma R&D blog provides insight and opinion on topics related to pharmaceutical research and development, namely: big data, target identification, new drug discovery, drug safety monitoring, risk mitigation and regulatory compliance. We serve the community of chemists, scientists, drug safety specialists, educators and students interested in pharmaceutical R&D.

Editor’s note: The views and opinions expressed are those of the author and do not necessarily reflect the views of Elsevier,  its affiliates and sponsors or its parent company, Reed Elsevier.