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关于驱动生命科学探索 AI 的模型的观点与深度解析
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This update enables structural biologists and protein engineers to accurately predict metal-ion and water binding sites through a simplified, guided workflow on Vecura, eliminating the need for complex local infrastructure or GPU setup.
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This update enables drug discovery researchers and computational chemists to predict protein–ligand binding affinity directly from sequence and molecular structure through a guided workflow inside Vecura, without setting up GPU clusters or complex technical infrastructure.
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This update enables structural biologists, antibody engineers, and drug discovery researchers to predict protein-protein docking poses from monomer structures through a guided workflow inside Vecura, without setting up complex GPU infrastructure or computing multiple sequence alignments.
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This update enables structural biologists, computational drug designers, and biotech researchers to predict how two protein structures dock into a complex—complete with residue-level flexibility and per-residue confidence scores—through a guided workflow inside Vecura, without setting up complex GPU infrastructure or multiple-sequence alignment pipelines.
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This update enables structural biologists and drug discovery researchers to predict cyclic peptide structures and their protein complexes through a guided workflow inside Vecura, eliminating the need to manage complex, resource-intensive local infrastructure.
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This update enables biologists, protein engineers, and drug discovery researchers to generate 3D protein structures directly from primary amino acid sequences through a streamlined workflow inside Vecura, eliminating the need to set up complex MSA pipelines or manage structural templates.
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This update enables computational biologists and drug discovery researchers to predict protein-ligand binding poses and affinity directly through a guided workflow on Vecura, eliminating the need to manage complex, resource-intensive infrastructure.
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This update allows structural biologists and computational researchers to predict 3D biomolecular complexes directly within Vecura, removing the burden of managing complex technical environments or infrastructure.
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This update enables antibody engineers and protein scientists to generate accurate 3D atomic structures of antibody variable domains directly through a guided workflow in Vecura, eliminating the need for complex local infrastructure and time-consuming manual setup.
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This update enables cheminformatics researchers and drug discovery scientists to generate molecular fingerprints and fine-tune property predictors through a guided workflow inside Vecura, without setting up complex GPU infrastructure or managing ChemProp installations.
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This update enables structural biologists and drug discovery researchers to predict complex biomolecular structures through a guided workflow inside Vecura, without setting up complex technical infrastructure.
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This update empowers computational biologists to rapidly generate protein conformational trajectories directly within the Vecura platform, streamlining structural analysis without the need for resource-intensive, traditional molecular dynamics setups.