NYB.AI Launches Vecura 2.0
Bringing Agentic AI Workflows to Molecular Discovery, with NVIDIA
Life science research is moving toward the AI era, but most discovery teams are still unable to deploy frontier models at scale. Advanced AI models, molecular simulation tools, scientific databases, inference optimisation, and GPU infrastructure are still dispersed across multiple systems. For many pharmaceutical businesses, ingredient innovators, biotech teams, and research organisations, AI adoption is gradual, expensive, and technically challenging.
NYB.AI, a Singapore-based Artificial Intelligence (AI) biotechnology startup that develops infrastructure for molecular discovery and life science research. It aims to bridge this gap with Vecura, an agentic AI platform that integrates scientific models, biological data, molecular analysis tools, and GPU-accelerated computing into a unified discovery workflow.
Vecura 2.0 expands on the earlier Vecura 1.0 platform, which offered AI models and scientific tools for molecular discovery. Vecura 2.0’s new agentic layer expands beyond tool access to workflow execution. It is intended to assist research teams in defining scientific objectives, retrieving important background, activating appropriate models, comparing outcomes, and producing structured decision support with minimal manual coordination.
From model access to agentic workflows
Vecura was founded on a simple premise: the next obstacle to AI for science is a lack of access to new models. It is the capacity to get such models to work together in discovery scenarios. The original Vecura platform integrated hundreds of AI models, scientific tools, biological data, and molecular analysis capabilities into connected workflows, allowing researchers to move seamlessly between compound analysis, target exploration, formulation research, toxicity assessment, and translational R&D without relying on separate systems at each stage.
Vecura 2.0 builds on this base with an agentic AI layer. The enhanced platform is intended to reason throughout the process by recognising research objectives, accessing relevant scientific context, selecting appropriate models, coordinating execution, comparing outputs, and generating structured next step recommendations. This transforms Vecura from a workflow platform to a more intelligent discovery system capable of assisting scientists at scale, bringing it closer to how real-world research decisions are made.
This agent-based strategy enables scientists to concentrate on research direction rather than infrastructure administration. It also helps the NYB.AI’s overarching goal is to democratise molecular discovery by making powerful AI models, scientific tools, and GPU-intensive procedures more available to teams outside of giant pharmaceutical companies and specialised computational labs.
Scaling access through NVIDIA technologies
Using NVIDIA technologies, NYB.AI is enhancing Vecura’s agentic architecture for scientific discovery. As a participant in the NVIDIA Inception program for startups, NYB.AI was granted access to NVIDIA Hopper GPUs via NVIDIA Innovation Lab in addition to engineering support to enhance Vecura’s capacity to organise, carry out, and organise intricate research projects. With the help of this assistance, Vecura is possible to transition from isolated model execution to linked workflows in which scientific context, model selection, inference, comparison, and decision support function as a single system.
NVIDIA accelerated computing powers a variety of NVIDIA technologies, including agentic orchestration, scientific modelling, data and retrieval, and production deployment. Additionally, a token-based access paradigm for Vecura 2.0 is being developed by NYB.AI. Biopharma businesses, ingredient companies, and research teams can access AI-powered discovery workflows like molecular docking, compound screening, and bioactivity prediction through platform credits. With this approach, users can use NYB to do computationally demanding scientific activities.AI’s platform without having to construct or manage their own infrastructure.
What’s next?
During InnoVEX 2026 (June 2–5, 2026, Taipei Nangang Exhibition Centre), NYB.AI presented Vecura 2.0 at the NVIDIA Inception Startup Showcase, at the Inception Pavilion. In addition to increasing its visibility within the NVIDIA Inception ecosystem and providing NYB.AI with a chance to validate Vecura 2.0 with enterprise leaders, investors, ecosystem partners, and foreign buyers, the showcase showed how agentic AI can advance beyond model experimentation into practical research infrastructure for molecular discovery teams.
NYB.AI showcased how Vecura 2.0 can facilitate molecular discovery and life science research in the fields of consumer health, pharmaceuticals, nutraceuticals, cosmetics, food science, and functional ingredients at InnoVEX 2026. The platform is a reflection of NYB.AI’s overarching goal to improve the usability, scalability, and accessibility of advanced AI discovery infrastructure throughout the life science ecosystem.