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4 Notable Life Sciences Trends from the Tech Trends Report
Posted on June 4th, 2021 by Ann-Marie Roche in AI & Data
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.
- AI Speeds Scientific Discovery
Given the extraordinarily high cost of drug discovery and development (one recent study put the price tag at $985 million to bring a drug to market), pharmaceutical companies are always in search of ways to make the process faster and less expensive. The report recognizes that an important trend has been the increasing use of AI to accelerate scientific discovery, noting that algorithms can be used to perform tedious and repetitive experiments and tasks that have traditionally fallen to graduate students. Indeed, it’s quite exciting to think of all that brain power that could instead be focused on more thought-provoking research responsibilities. Scalability remains a major challenge to using AI in this way, but a platform like SciBiteAI, for example, can assist in tackling the problem by enabling the delivery of structured, scalable and repeatable AI.
- AI-First Drug Discovery
While established pharmaceutical companies are learning how to adopt and integrate AI into their R&D departments, many new start-ups are building their entire business on an AI foundation. For example, the innovative start-up Exscientia, which Elsevier has supported through the Hive initiative (as discussed in this PharmaTimes article), was the first company to have an AI-designed molecule, DSP-1181, go into clinical trials. The report points out that AI-first drug companies are very attractive to investors, and, for that matter, to major pharma firms – nearly all of whom have already forged partnerships with AI drug discovery start-ups. Earlier this spring, Fortune reported that investment in companies and projects using machine learning in drug discovery have grown to $13.8 billion (more than 4.5 times what was invested in 2019!).
- NLP Algorithms Detect Virus
Natural language processing technology helps life science researchers increase efficiencies and get answers faster (read how NLP extracts valuable information from patents here). Semantic-based machine learning goes hand-in-hand with subject matter expertise, the two working together to help researchers uncover needed insights. But NLP algorithms aren’t limited to improving text searching – they can also interpret genetic changes in viruses. The report shares an example of computational biologists at MIT modeling “viral escape” (i.e. a virus’s ability to mutate and escape the immune system) using NLP in order to strategize before mutations actually occur. The possibility of using NLP to prevent or better manage viral spread is especially enticing now, as the world continues battling Covid and hopes to be better prepared for the next viral threat.
- Protein Folding
The report highlights how DeepMind, an AI company that was acquired by Google, recently announced that it had created a way to test and model the complex folding patterns of amino acid chains with an AI system. Known as AlphaFold, this is the solution to the “protein folding problem” that biologists have studied for decades. Although there is still much to learn following this enormous breakthrough, the AlphaFold team sees it as having a major impact in areas like drug design and environmental sustainability. Find out more about AlphaFold here.
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