Updates
Perspectives and deep dives on the models powering AI for life science discovery.
Updates
This integration allows medicinal chemists and drug discovery researchers to perform reaction-informed scaffold decoration and multi-objective molecular scoring within Vecura, eliminating the need to manage complex generative AI infrastructure.
Updates
This update enables structural biologists and bioinformatics researchers to perform accurate multiple structure alignments at scale through a guided workflow on Vecura, removing the burden of managing complex C++ infrastructure.
Updates
The REST2-AMP/MM framework is now available on Vecura, allowing computational biologists and researchers to perform quantum-accurate multiscale molecular dynamics without the burden of building complex simulation infrastructures.
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.
Updates
This update enables computational biologists and medicinal chemists to streamline lead discovery and protein engineering through a guided DSMBind workflow inside Vecura, eliminating the need for complex local infrastructure setup.
Updates
This update enables single-cell researchers to generate universal, cross-species cell embeddings directly through a guided workflow on Vecura, eliminating the need for complex GPU infrastructure and manual model setup.
Updates
This update enables medicinal chemists and computational researchers to generate novel, 3D drug-like ligands tailored to specific protein binding pockets directly through a guided Vecura workflow, eliminating the need for complex infrastructure management.
Updates
This update enables medicinal chemists and computational scientists to rapidly estimate the aqueous solubility of small molecules directly within Vecura, streamlining ADMET profiling and library triage without the need for complex local infrastructure.
Updates
This integration enables researchers and bioinformaticians to easily generate advanced gene-level embeddings from single-cell RNA-seq data within the Vecura platform, bypassing the need for complex deep-learning infrastructure.
Updates
This update empowers researchers to perform de novo protein and cyclic peptide design through a streamlined workflow on Vecura, eliminating the need for complex technical infrastructure or custom software maintenance.
Updates
Drug discovery professionals and computational chemists can now explore chemical space and optimize lead scaffolds using the MoFlow generative model, all through a streamlined, infrastructure-free workflow on Vecura.
Updates
This update allows antibody engineers and researchers to instantly assess the developability of their nanobody candidates through a streamlined, guided workflow on Vecura, removing the need for local software configuration.