Transformation
As we step into 2025, R&D organizations are bracing for another year of rapid-pace, transformative shifts.
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Learn how AI Supermodels are allowing for faster, more accurate predictions with far fewer data points.
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In today’s competitive R&D landscape, selecting the right technology partner is one of the most critical decisions your organization can make.
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Leveraging advanced technology like generative AI through digital transformation (not digital enhancement) is how to get the biggest returns in scientific R&D.
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There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.
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In the rapidly-evolving landscape of drug discovery and development, traditional approaches to R&D in biopharma are no longer sufficient. Artificial intelligence (AI) continues to be a game-changer.
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Leveraging large language models (LLMs) in materials science and chemical R&D isn’t just a speculative venture for some AI future. There are two primary use cases that are ready for adoption in research labs today.
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R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge towards groundbreaking discoveries.
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Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data fabric as part of their technology solution set.
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By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over the world from both academia and industry. Some of my team and I recently returned from their primary annual convening, the ACS 2023 Fall Meeting, held in San Francisco. I…
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