Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Accelerating synthetic chemistry with a predictive retrosynthesis solution
Posted on August 12th, 2021 by Ann-Marie Roche
In most aspects of life, we think of “endless possibilities” as a good thing. But in science, the wealth of possibilities can be daunting. Pharmaceutical researchers typically start with a disease that they want to tackle, find a target, and then try to identify compounds which interact with that target. There may be around 200-300 drug candidates per project, and synthesis planning and execution usually takes four to eight weeks per drug candidate. This process requires many chemists and a huge, costly effort to proceed.
(more…)Confronting the Problem of AI Bias
Posted on June 18th, 2021 by Ann-Marie Roche
When thinking of artificial intelligence (AI), many people rely on cultural touchstones from science fiction films and TV. They might think of HAL, the calm-voiced AI from 2001: A Space Odyssey, or Data, the even-tempered android from Star Trek. We tend to imagine that artificial intelligence will be driven by reason, logic and facts – not by the emotions and prejudices that get us regular humans into trouble so often.
(more…)4 Notable Life Sciences Trends from the Tech Trends Report
Posted on June 4th, 2021 by Ann-Marie Roche
The Future Today Institute has released its 2021 Tech Trends Report (14th Annual Edition), a comprehensive report on strategic trends that it anticipates will affect business, government, education, media and society in the coming year. We noted four trends discussed in the report’s Health, Medicine & Science section that will be of particular interest to those in the pharmaceutical and life sciences sectors.
(more…)Mutually empowering – semantic-based machine learning and subject matter expertise
Posted on May 7th, 2021 by Lauren Barham
In a day dedicated to emerging science and technologies at the Pistoia Alliance virtual conference Collaborative R&D in Action, SciBite CTO James Malone opened the program with a compelling exploration of use cases for semantic-based machine learning (ML). A simple but elegant ML strategy based on “seeding” named entity recognition (NER) can facilitate ontology creation, drive language translation, take a crack at gaining insights from social media platforms, and generate answers to questions faster. His most important take-away: semantic-based ML and subject matter expertise (SME) are mutually empowering.
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