Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
AI-driven innovation in life sciences: Unlocking the page – 20+ years of digital innovation at Elsevier
Posted on December 2nd, 2021 by Ann-Marie Roche in 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.
(more…)AI-driven innovation in life sciences: Data as data should be
Posted on November 17th, 2021 by Ann-Marie Roche in 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.
(more…)AI-driven innovation in Life Sciences: Data Science & AI amplifies your innovative drug R&D capabilities
Posted on November 4th, 2021 by Ann-Marie Roche in 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.
(more…)At the leading edge of an innovation hotspot: predictive reaction modeling
Posted on September 8th, 2021 by Louise Springthorpe in Chemistry
Indisputably, a surge in predictive machine learning models to support synthetic chemistry has galvanized a movement to make these tools effective R&D solutions. Reaxys has been part of this innovation journey from the beginning. Today, Reaxys includes an award-winning Predictive Retrosynthesis solution that merges deep neural networks trained on Reaxys data with a Monte Carlo tree search to quickly discover promising candidate routes. Users explore these routes and new synthetic spaces via an intuitive interface that links to Reaxys content on commercial availability and accelerates design-make-test-analyze (DMTA) cycles.
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