# NYB.AI Launches Vecura 2.0, Bringing Agentic AI Workflows to Molecular Discovery, with NVIDIA

- Link: https://www.thailand-business-news.com/pr-news/nyb-ai-launches-vecura-2-0-bringing-agentic-ai-workflows-to-molecular-discovery-with-nvidia-2
- Published: 2026-06-02T11:00:00+07:00
- Author: PR Newswire

TAIPEI, June 2, 2026 /PRNewswire/ — Life science research is entering an AI era,
but most discovery teams still cannot use frontier models at scale. Advanced AI 
models, molecular simulation tools, scientific databases, inference optimization
and GPU infrastructure remain fragmented across separate systems. For many pharmaceutical
companies, ingredient innovators, biotech teams, and research groups, this makes
AI adoption slow, costly, and technically demanding.

 [ ⌊Agentic AI for scientific execution. Vecura 2.0 moves beyond simple prompts and responses into autonomous scientific coordination.⌉ ](https://mma.prnasia.com/media2/2992855/AI_Agentic_AI__Vecura_2_0.html)

Agentic AI for scientific execution. Vecura 2.0 moves beyond simple prompts and 
responses into autonomous scientific coordination.

NYB.AI, a Singapore Artificial Intelligence (AI) technology startup developing infrastructure
for molecular discovery and life science research, aims to address this gap with
Vecura, its agentic AI platform that connects scientific models, biological data,
molecular analysis tools, and GPU accelerated computing into a unified discovery
workflow.

Vecura 2.0 builds on the original Vecura 1.0 platform, which provided access to 
AI models and scientific tools for molecular discovery. With the new agentic layer,
Vecura 2.0 moves beyond tool access toward workflow execution. It is designed to
help research teams define scientific objectives, retrieve relevant context, activate
suitable models, compare outputs, and generate structured decision support with 
less manual coordination.

**From model access to agentic workflows**

Vecura was built around a simple premise: the next barrier in AI for science is 
not access to more models. It is the ability to make those models work together 
in discovery settings. The original Vecura platform brought hundreds of AI models,
scientific tools, biological data, and molecular analysis capabilities into connected
workflows, helping research teams move across compound analysis, target exploration,
formulation research, toxicity assessment, and translational R&D without relying
on separate systems at every step.

Vecura 2.0 extends this foundation with an agentic AI layer. The upgraded platform
is designed to reason across the workflow by understanding research objectives, 
retrieving relevant scientific context, selecting suitable models, coordinating 
execution, comparing outputs, and generating structured next step recommendations.
This evolves Vecura from a workflow platform into a more intelligent discovery system
that can assist scientists at scale, closer to how real research decisions are made.

This agentic approach allows scientists to focus on research direction rather than
infrastructure management. It also supports NYB.AI’s broader vision to democratize
molecular discovery by making advanced AI models, scientific tools, and GPU intensive
workflows more accessible to teams beyond large pharmaceutical organizations and
specialized computational labs.

**Scaling access through NVIDIA technologies**

NYB.AI is strengthening Vecura’s agentic architecture for scientific discovery using
NVIDIA technologies. As a member of the [NVIDIA Inception](https://www.nvidia.com/en-us/startups/)
program for startups, [NYB.AI](http://nyb.ai/) received access to [NVIDIA Hopper](https://www.nvidia.com/en-us/data-center/h100/)
[ GPUs](https://www.nvidia.com/en-us/data-center/h100/) through [NVIDIA ](https://www.nvidia.com/en-us/data-center/innovation-lab/)
[Innovation Lab](https://www.nvidia.com/en-us/data-center/innovation-lab/), along
with engineering guidance to advance Vecura’s ability to plan, execute, and coordinate
complex research tasks. This support enables Vecura to move beyond isolated model
execution toward connected workflows where scientific context, model selection, 
inference, comparison, and decision support operate as one system.

Vecura is powered by NVIDIA technologies spanning agentic orchestration, scientific
modeling, data and retrieval, and production deployment – all running on [NVIDIA accelerated computing](https://www.nvidia.com/en-us/data-center/solutions/accelerated-computing/).

NYB.AI is also developing a token based access model for Vecura 2.0. Through platform
credits, biopharma companies, ingredient companies, and research teams can access
AI powered discovery workflows such as compound screening, bioactivity prediction,
and molecular docking. This model allows users to run compute intensive scientific
tasks through NYB.AI’s platform without needing to build or operate their own infrastructure.

**What’s next?**

NYB.AI is bringing Vecura 2.0 to the NVIDIA Inception Startup Showcase at the Inception
Pavilion during InnoVEX 2026 (June 2–5, 2026, Taipei Nangang Exhibition Center).
The showcase will give NYB.AI an opportunity to validate Vecura 2.0 with enterprise
leaders, investors, ecosystem partners, and international buyers, and strengthens
its visibility within NVIDIA Inception ecosystem — demonstrating how agentic AI 
can move beyond model experimentation into usable research infrastructure for molecular
discovery teams.

At InnoVEX 2026, NYB.AI will demonstrate how Vecura 2.0 can support molecular discovery
and life science research across pharmaceuticals, nutraceuticals, cosmetics, food
science, functional ingredients, and consumer health. The platform reflects NYB.
AI’s broader mission to make advanced AI discovery infrastructure more usable, scalable,
and accessible across the life science ecosystem.

**About NYB.AI**

NYB.AI is an AI biotechnology company developing infrastructure for molecular discovery
and life science research. Its flagship platform, **Vecura**, is an agentic AI platform
that connects AI models, scientific tools, biological data, retrieval systems, accelerated
computing, and autonomous workflows to help life science teams shorten discovery
cycles and prioritize high-potential candidates.

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