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BIOVIA Self Learning

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Read the BIOVIA blog on the latest news and innovations from our experts.

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Forcefield Optimization for Energetic Materials

Discover how BIOVIA’s computational tools advance the study of molecular crystals, including crystal structure prediction, lattice energy calculations, and the latest COMPASSIII forcefield developments.

Article

Forcefield Optimization for Energetic Materials
Development of Innovative Drugs Using Materials Studio

Discover how BIOVIA Materials Studio, with the Martini 3 force field, enhances lipid nanoparticle and membrane simulations for advanced drug delivery systems. Explore the benefits of molecular dynamics and coarse-grained simulations in optimizing therapeutic formulations.

Article

Development of Innovative Drugs Using Materials Studio
Faster Onboarding with AI Assistants

Discover how integrating large language models enhances onboarding and boosts productivity by offering expert, on-demand assistance for designing and running protocols.

Article

Faster Onboarding with AI Assistants
NMR Crystallography – Solving Organic and Inorganic Structures

NMR crystallography excels in detailing short-range structures in materials using advanced spectrometers and computational tools, with applications in fields like fuel cells and pharmaceuticals, and continues to grow through integrated experimental and computational techniques.

Article

NMR Crystallography – Solving Organic and Inorganic Structures
Empowering Drug Discovery with Machine Learning Workbench

Machine Learning Workbench is a cutting-edge, no-code application designed for data scientists and computational scientists working in the life sciences field. It offers a streamlined, intuitive solution for creating ML models and training pipelines on the 3DEXPERIENCE platform.

Article

Empowering Drug Discovery with Machine Learning Workbench
Learning To Be Narrow

Leveraging BIOVIA for efficient QSAR modeling, showcasing narrow AI’s continued relevance in chemistry amidst general-purpose AI advancements.

Article

Learning To Be Narrow

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