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On Wed, 12 Feb, 8:03 AM UTC
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Capgemini's AI Breakthrough to Accelerate Bioeconomy
This new approach, powered by a specialised protein large language model, predicts the most effective protein variants while using 99% less data than traditional methods. Capgemini, on Tuesday, introduced a generative AI-driven method for protein engineering that significantly reduces data requirements, making scientific breakthroughs faster and more accessible. This new approach, powered by a specialised protein large language model (pLLM), predicts the most effective protein variants while using 99% less data than traditional methods. With a patent pending, this breakthrough is expected to drive innovation across industries such as healthcare, agriculture, and environmental science. Capgemini's methodology tackles one of the biggest challenges in biotechnology research -- the data bottleneck -- by enabling discoveries with smaller datasets. This means organisations can develop new bio-solutions even in resource-limited environments. By reducing research costs and time, this approach allows businesses to explore solutions that were previously too expensive or impractical. Roshan Gya, CEO of Capgemini Invent, emphasised the impact of this development: "Our new methodology is faster, more cost-effective and opens the door to new opportunities for clients to develop innovative bio-based solutions." The approach enhanced a cutinase enzyme that breaks down PET plastic by 60%, making it easier and more cost-effective to degrade plastic waste. This could help reduce environmental pollution and lower waste management costs. AI-driven predictions reduced the number of experiments needed to improve the Green Fluorescent Protein (a widely used research marker) from thousands to just 43 while increasing its brightness by seven times. This efficiency can speed up drug discovery, diagnostics, and bioengineering applications. The breakthrough has drawn praise from experts in the field. Stephen Wallace, professor of chemical biotechnology at the University of Edinburgh, highlighted its significance: "Capgemini's generative AI-driven approach represents a significant leap in protein engineering. By drastically reducing data requirements, Capgemini has fundamentally transformed the innovation timeline in bioengineering." He further mentioned that this breakthrough reflects a clear vision for the future of engineering biology, leveraging the design and engineering of new biocatalysts to enable more sustainable and scalable industrial processes. With its expertise and adaptability, Capgemini is well-positioned to drive technological advances in this exciting and rapidly evolving interdisciplinary field. This breakthrough was developed in Capgemini's AI-driven biotechnology lab at Cambridge Consultants in the UK. For over ten years, Cambridge Consultants has been pioneering engineering biology and AI development. The lab brings together experts in biology, chemistry, AI, digital twins, electronics, and sustainability to push the boundaries of scientific discovery.
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Capgemini reveals gen AI-driven breakthrough to accelerate the bioeconomy
New methodology reduces the data requirements for protein engineering by 99% so organizations can unlock innovation even in resource-constrained environments Capgemini today announced a new generative AI-driven methodology for protein engineering that uses a specialized protein large language model (pLLM) to predict the most effective protein variants. With a patent pending,[1] this novel approach will help accelerate the advancement of the global bioeconomy[2] and drive critical scientific breakthroughs across industries including healthcare, agriculture, and environmental science. By reducing the datapoints required to design protein sequences by over 99%, the new methodology harnesses the power of generative AI (gen AI) to drastically reduce the time and resources needed for research and development (R&D). Using this approach, Capgemini can help clients reduce the development cost of biosolutions and unlock business cases that were not previously viable. Breakthrough solves the data bottleneck challenge Advancements in engineering biology[3] are expected to disrupt all industries, with half of business leaders predicting this transformation will happen within the next five years.[4] However, data can be a critical bottleneck in research timelines. This new methodology makes scientific breakthroughs possible with significantly smaller data sets, enabling organizations to innovate even in resource-constrained environments. Using this novel approach, Capgemini is exceptionally positioned to help clients find and develop innovative solutions to global challenges such as disease, food security, and climate concerns. The methodology was created in the bespoke gen AI-driven biotechnology lab of Cambridge Consultants, the deep tech powerhouse of the Capgemini Group. The methodology was applied to several critical use cases to demonstrate how it could drive a step-change in innovation. Examples that can be readily translated to other applications include: 60% increase in plastic degradation efficiency: Capgemini's gen AI-driven approach enhanced the cutinase enzyme, increasing its ability to break down PET plastic by 60%. This advancement is one example of how protein engineering can create novel, highly efficient and cost-effective solutions to tackle global plastic waste. By making it easier to degrade plastic, this breakthrough can support sustainability objectives and help lower operational costs associated with waste management. Reduced experimentation for faster innovation: Using gen AI predictions, Capgemini reduced the number of experiments needed to identify an improved variant of the commonly cited Green Fluorescent Protein benchmark, from thousands to just 43 data points, achieving a brightness level seven times greater than that of the natural jellyfish protein. This significantly cuts down on the time and resources typically required for experimental testing, enabling quicker deployment across a range of fields, from accelerating drug discovery and enhancing diagnostic tools to advancing bioengineering applications. "Capgemini's proprietary generative AI-driven approach means we are uniquely placed to enable clients to significantly accelerate their bio-journey in previously untapped areas and, crucially, contribute to helping solve many of humanity's most pressing challenges," said Roshan Gya, CEO of Capgemini Invent and member of the Group Executive Board. "Our new methodology is faster, more cost-effective, and opens the door to new opportunities for clients to develop innovative bio-based solutions. The Capgemini Group delivers end-to-end engineering biology and scale-up capabilities so that our clients can derive significant business value and develop proprietary IP, moving away from traditional carbon-based approaches and fueling growth in the bioeconomy." Prof. Stephen Wallace, Professor of Chemical Biotechnology at the University of Edinburgh, stated: "Capgemini's generative AI-driven approach represents a significant leap in protein engineering. By drastically reducing data requirements, Capgemini has fundamentally transformed the innovation timeline in bioengineering. This breakthrough reflects a clear vision for the future of engineering biology, leveraging the design and engineering of new biocatalysts to enable more sustainable and scalable industrial processes. With its expertise and adaptability, Capgemini is well-positioned to drive technological advances in this exciting and rapidly evolving interdisciplinary field." Building on 10 years of pioneering engineering biology and AI development, the bespoke AI-driven biotechnology lab at Cambridge Consultants has been created at its UK headquarters, home to an unrivalled combination of multidisciplinary experts in biology, chemistry, gen AI, digital twins, electronics, software, sustainability and more. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of €22.5 billion.
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Capgemini introduces a generative AI-powered method for protein engineering that reduces data requirements by 99%, potentially revolutionizing biotechnology research across healthcare, agriculture, and environmental science.
Capgemini has unveiled a groundbreaking generative AI-driven methodology for protein engineering that promises to revolutionize the bioeconomy. This innovative approach, which utilizes a specialized protein large language model (pLLM), can predict the most effective protein variants while reducing data requirements by an astounding 99% compared to traditional methods 12.
One of the most significant hurdles in biotechnology research has been the data bottleneck, which often slows down scientific breakthroughs. Capgemini's new methodology tackles this challenge head-on by enabling discoveries with substantially smaller datasets. This advancement allows organizations to innovate even in resource-constrained environments, potentially accelerating research and development across various industries 1.
The potential of this AI-driven approach has been demonstrated through several critical use cases:
Plastic Degradation Efficiency: Capgemini enhanced a cutinase enzyme, increasing its ability to break down PET plastic by 60%. This improvement could lead to more efficient and cost-effective solutions for tackling global plastic waste 12.
Green Fluorescent Protein Enhancement: Using AI predictions, researchers reduced the number of experiments needed to improve the Green Fluorescent Protein from thousands to just 43, while increasing its brightness sevenfold. This efficiency gain could accelerate drug discovery, diagnostics, and bioengineering applications 12.
The implications of this breakthrough extend far beyond the lab. By significantly reducing the time and resources required for protein engineering, Capgemini's methodology opens up new possibilities for innovation in healthcare, agriculture, and environmental science. It enables businesses to explore solutions that were previously too expensive or impractical, potentially driving a new wave of bio-based innovations 12.
Professor Stephen Wallace of the University of Edinburgh has praised the breakthrough, highlighting its potential to transform innovation timelines in bioengineering. He emphasized that this approach reflects a clear vision for the future of engineering biology, particularly in developing sustainable and scalable industrial processes 12.
With a patent pending for this methodology, Capgemini is well-positioned to lead in the rapidly evolving field of AI-driven biotechnology. The company's AI-driven biotechnology lab at Cambridge Consultants in the UK brings together experts from various disciplines, including biology, chemistry, AI, and sustainability, to push the boundaries of scientific discovery 12.
As the bioeconomy continues to grow, with half of business leaders predicting industry-wide disruption within the next five years, Capgemini's breakthrough could play a crucial role in accelerating this transformation. By enabling faster, more cost-effective research and development, this AI-driven approach may help solve some of humanity's most pressing challenges in health, food security, and environmental conservation 2.
Reference
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Capgemini introduces 'Augmented Engineering' solutions leveraging Generative AI to accelerate innovation and streamline processes in engineering and R&D across various industries.
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C3 AI and Capgemini have announced an expanded partnership to enhance the delivery of Enterprise AI solutions across various industries, aiming to improve efficiency, productivity, and cost reduction for businesses worldwide.
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Capgemini expands its Intelligent App Factory on Azure, collaborating with Mistral AI and Microsoft to drive the adoption of generative AI technologies, focusing on customized solutions for regulated industries.
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Google DeepMind has introduced AlphaProteo, an advanced AI model for protein design. This breakthrough technology promises to accelerate drug discovery and development of sustainable materials.
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2 Sources
Researchers develop EVOLVEpro, an AI tool that significantly enhances protein engineering capabilities, promising advancements in medicine, agriculture, and environmental solutions.
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