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Artificial Intelligence involves the science and engineering of developing intelligent machines
The manufacturing world is at a cusp of a new revolution that will redefine conventional business models
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The TOEs are a great source of information which gives corporates critical information on companies to action for their strategic investments either as an open source platform or to collaborate and to develop successful products
A Benchmarking System to Spark Companies to Action—Innovation that Fuels New Deal Flow and Growth Pipelines
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Pharmaceutical drug discovery and development suffers from declining success rates with new molecules, and the rate of return has shrunk from 16% in 2011 to almost 11% in 2018. Frost & Sullivan finds that traditional solutions focused primarily on data from limited sources and rule-based computational techniques used to address the understanding of targets and leads are inefficient. Artificial intelligence (AI) is set to transform the landscape of drug discovery. The application of AI-based products and solutions is enabling the pharmaceutical industry to shorten discovery timelines, enhance process agility, increase prediction accuracy on the efficacy and safety of drugs, and improve the opportunity to diversify drug pipelines using a cost-effective model.Most vendors are focused on collecting, creating, and augmenting data from across laboratories, clinical trials, real-world evidence, biobanks, and repositories. The increasing volume and veracity of clinical and research data compels traditional providers to leverage enabling tools and technologies such as cloud computing, artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and advanced analytics to make a shift from the slow, traditional approach to a relatively fast, rational data-driven drug discovery and development approach.To remain competitive it is critical for players to establish the right balance of data, AI, and computational capability and to match it with the wet-lab capability. There remains inadequate understanding of the biological networks and drug-target interactions; here, AI has been able to support the identification and prioritization of disease-specific therapeutic targets based on gene-disease associations. Such results must be replicated and validated through in-vitro experiments and in-vivo models.Frost & Sullivan finds that the impact of AI on the complete pharma value chain can more than double what is achievable using traditional analytics and capture between 2 and 3% of industry revenue, amounting to more than $50 billion in potential annual impact. This Frost Radar™ recognizes industry participants that are at the forefront of developing and successfully employing advanced tools. This industry-first benchmarking study provides an introduction to the ecosystem and recognizes pioneering companies. The Radar™ reveals the market positioning of each company using its Growth and Innovation scores as highlighted in the Frost Radar™ methodology. The document presents competitive profiles on each company based on its strengths, opportunities, and market positioning. We discuss strategic market imperatives and the competitive environment that vendors operate in as well as make recommendations for each provider to spur growth.Author: Amol Dilip Jadhav
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