Updates
Perspectives and deep dives on the models powering AI for life science discovery.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.
Updates
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.