Tag: alphafold
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Achieving Cost-Efficient Boltz-1 Simulations on Fovus at $0.1 per Biomolecular Structure Prediction
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Boltz-1 is a state-of-the-art open-source model that predicts biomolecular structures, including proteins, RNA, DNA, and other molecular interactions. Unlike its proprietary counterparts, it also supports modified residues, covalent ligands, and glycans, allowing for a broader range of applications. By conditioning predictions on specified interaction pockets or contacts, Boltz-1 offers a level of flexibility that makes…
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Case Study: Intelligently Running AlphaFold 3 on Fovus with an AI-Optimized HPC Strategy, Saving up to 67% Cost and 45% Time
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AlphaFold 3 is a powerful AI-driven protein structure prediction tool developed by DeepMind. Building upon the success of its predecessors, AlphaFold 3 extends beyond protein folding and incorporates broader molecular modeling capabilities. It is widely used in computational biology to predict protein-protein interactions, drug discovery, and large-scale proteomics research. AlphaFold 3 is particularly valuable in…
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Revolutionizing Protein Structure Prediction with AI and HPC: Utilizing AlphaFold
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Predicting protein structures accurately has long been a significant challenge in biological research. Proteins, essential to nearly all biological processes, derive their functions from their three-dimensional structures. Understanding these structures enables drug discovery, genetic research, and biotechnology advancements. However, determining protein structures experimentally through methods like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryo-electron…