更新
关于驱动生命科学探索 AI 的模型的观点与深度解析
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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.
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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.
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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.
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We are excited to bring the powerful Psi4 quantum chemistry suite to the Vecura platform, allowing researchers to perform high-accuracy molecular simulations and energy calculations through an intuitive, streamlined interface.
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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.
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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.
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This update enables medicinal chemists and structural biologists to generate novel, 3D drug-like ligand candidates directly within their target binding pockets, accessible via a streamlined workflow on Vecura without the need for complex local infrastructure.
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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.
产品更新
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.
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This update enables researchers and medicinal chemists to rapidly predict and prioritize ligand-binding pockets through a guided workflow inside Vecura, eliminating the need to manage complex local software installations or infrastructure.
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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.
产品更新
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.