更新
关于驱动生命科学探索 AI 的模型的观点与深度解析
产品更新
Vecura users can now perform early-stage in silico viscosity screening of monoclonal antibody candidates, identifying potential high-viscosity liabilities without the need for complex software installation or physical experimental material.
产品更新
This update empowers structural biologists and drug discovery researchers to perform rapid, rigid-body protein-protein docking directly through an intuitive workflow on Vecura, eliminating the need for complex computational infrastructure and lengthy pose-sampling pipelines.
产品更新
This update allows protein engineers to optimize nanobody sequences and evaluate structural region stability directly through an intuitive, automated workflow on Vecura, eliminating the need for complex local infrastructure.
产品更新
This update enables structural biologists and bioinformaticians to perform high-speed protein structure searching and large-scale clustering directly through a streamlined, guided workflow on Vecura, eliminating the need to manage complex, resource-intensive computational infrastructure.
产品更新
We are excited to announce that FAMPNN (Full-Atom MPNN) is now available on Vecura, empowering protein engineers and researchers to perform full-atom sequence design, sidechain packing, and mutation scoring within a streamlined, guided workflow.
产品更新
This update empowers protein engineers and computational biologists to conduct advanced protein analysis and variant effect prediction through an intuitive, guided workflow on Vecura, bypassing the need for complex local infrastructure.
产品更新
This update enables protein engineers and computational biologists to analyze, score, and optimize paired antibody sequences through a guided workflow inside Vecura, bypassing the need for complex local infrastructure setup.
产品更新
This update enables structural biologists and researchers to predict the 3D architecture of protein complexes through a simplified, high-performance workflow on Vecura, eliminating the need for local technical infrastructure.
产品更新
This update brings HMMER's powerful profile hidden Markov model technology to Vecura, enabling researchers to conduct sensitive protein homology searches and domain annotations directly through a streamlined interface without the need for manual infrastructure management.
产品更新
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.
产品更新
Researchers can now identify potential small-molecule binding sites on proteins directly within Vecura, utilizing AF2BIND's advanced structural analysis to streamline early-stage drug discovery without the need for complex local infrastructure.
产品更新
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.