アップデート
ライフサイエンスの発見のための AI を支えるモデルに関する視点と深掘り。
アップデート
This update enables antibody engineers and structural biologists to easily predict the conformational flexibility of CDR3 loops directly through a guided workflow on Vecura, eliminating the need for complex local infrastructure setup.
アップデート
This update allows drug discovery researchers to perform state-of-the-art protein-ligand binding affinity scoring directly within the Vecura platform, eliminating the need to manage complex GPU-based inference pipelines.
アップデート
This update empowers researchers and bioinformaticians to perform rapid, high-accuracy transcript-level RNA-seq quantification directly within the Vecura platform, eliminating the need for complex, manual software environment setup.
アップデート
This update enables bioinformaticians, genomic researchers, and structural biologists to perform fast and highly accurate multiple sequence alignments of DNA, RNA, and protein sequences through a guided workflow inside Vecura, without setting up complex technical infrastructure.
アップデート
This update enables biotech researchers and protein engineers to generate novel protein sequences and evaluate their fitness through a guided workflow inside Vecura, without setting up complex GPU infrastructure or managing large-scale language model deployments.
アップデート
This integration allows bioinformaticians and genomics researchers to harness the power of the Evo 2 DNA language model directly within Vecura, simplifying the execution of complex genomic tasks like variant-effect prediction and sequence generation without the burden of infrastructure management.
アップデート
This update enables medicinal chemists and computational researchers to rapidly estimate protein-ligand binding affinities through a streamlined workflow on Vecura, eliminating the need to manage complex, structure-dependent simulation pipelines.
アップデート
This update allows structural biologists and protein engineers to perform sophisticated all-atom protein complex design and folding directly within Vecura, eliminating the need to manage complex GPU-intensive infrastructure.
アップデート
This update empowers protein engineers and computational biologists to instantly predict protein thermostability from sequence data within Vecura, eliminating the need for complex local infrastructure.
アップデート
This update enables oncology researchers and drug discovery scientists to perform rapid in-silico screening of anti-cancer drug combinations directly within Vecura, eliminating the need for complex local infrastructure setup.
アップデート
This update enables bioinformaticians and drug discovery scientists to perform molecular modeling and drug-target interaction screening through a guided workflow inside Vecura, eliminating the need to set up complex technical infrastructure.
アップデート
The integration of DLKcat into Vecura allows researchers in systems biology and metabolic engineering to seamlessly estimate enzyme turnover numbers (kcat) through a streamlined interface, eliminating the need to manage complex Python dependencies or local inference environments.