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SK Bioscience, a South Korean biotech company, has implemented a clinical Laboratory Information Management System (LIMS) based on BIOVIA ONE Lab.

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Discovery Studio, BIOVIA’s most extensive 3D modeling and simulation solution for drug discovery, is now available both as Software as a service (SaaS) and an on-prem application.

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Discover the journey of Nabin Giri, Scientific Software Development Intern at BIOVIA. Learn how Nabin leveraged AlphaFold for Protein Conformational Sampling in this video. Explore the challenges and rewards of a career in scientific software development at Dassault Systèmes.

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In the ever-evolving world of Chemistry, the shift towards compound libraries over individual elements is driving the demand for efficient tools like BIOVIA Pipeline Pilot.

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Explore the concept of ‘Neomania’ by Nassim Nicolas Taleb and Rolf Dobelli. Discover how the Pipeline Pilot Chemistry SDK leverages new features and Python scripting to enhance innovation and simplify code, driving progress in chemistry software development.”

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AI has many different applications in chemistry, with various concepts and databases, which AI handles being far too complex for even the most experienced chemists. To tackle these problems, significant advancements have been made by AI in many different, if not all, fields in chemistry.

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A guide to accelerating Time to Value (T2V), maximizing Return on Investment (ROI), and lowering Total Cost of Ownership (TCO).

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Understanding Proton transfer in molecules, both in the gas phase and in the solid state, can proceed quite quickly with a low energy barrier and a small energy difference between the initial and final states. As a result, numerous molecules can exist as different tautomers...

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World Oceans Day by United Nations is a reminder of the importance of oceans and ocean life for the rest of our planet. This year the theme of this day is Planet Ocean, which embodies the desire to put the ocean first in our work of protecting the natural environment.

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BIOVIA’s Contract Research team utilizes machine learning to address complex challenges faced by clients in the fields of energy, materials, consumer goods, aerospace, healthcare, and life sciences. They have developed Advanced Technology Capability (ATC) assets and have solved problems ranging from designing new polymer terminating agents to predicting the remaining useful life of batteries.

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Here we’ll examine how imaging analytics can provide accurate and informative data for applying AI to the additive manufacturing process. This will enable the vision of model-based predictive control for this process to become a reality. By using this approach, manufacturers can quickly and accurately identify potential areas of improvement and optimize their additive manufacturing process.

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