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 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.
アップデート
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.
アップデート
This integration enables medicinal chemists and structural biologists to perform high-precision protein-ligand docking and virtual screening directly through Vecura, eliminating the need for complex software stack installation or high-end infrastructure management.
アップデート
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.
アップデート
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.
アップデート
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.
アップデート
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.
アップデート
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.
アップデート
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.
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
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.
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.