What’s New on Vecura | September 21–25, 2026

Token-aware workflow runs, larger Gnina docking libraries, clearer pre-run validation, more reliable Agent chats, and seven new scientific tools.

Welcome to What’s New on Vecura, our regular recap of recent platform improvements and scientific capabilities.

This week, Vecura makes it easier to plan token use across multi-step workflows, configure larger Gnina docking runs, catch incompatible inputs before a run begins, and continue Agent chats with greater reliability.

Here is what’s new from September 21 to September 25, 2026.

Tool Runs and Workflows

Plan token use before executing a workflow

Vecura now provides estimated token consumption before a workflow is executed.

When the available token balance may not cover a planned step, the workflow provides a clear warning. If a step cannot run because of insufficient tokens, the run explains why, and retrying continues from the step that did not run rather than restarting completed work.

Results that cannot be accessed because tokens are unavailable now display clearer guidance instead of a raw error message.

Get clearer pre-run guidance for Making-it-Rain

Making-it-Rain now performs additional input checks before a run is dispatched.

Unsupported inputs are identified earlier, helping researchers understand what needs to be adjusted before computation begins.

Run Gnina docking with up to 100,000 compounds

Gnina now supports up to 100,000 compounds in a single docking run.

The ligand input field also handles larger compound lists more reliably, making it easier to configure high-volume docking experiments.

Seven additional scientific tools now available

Vecura has added seven additional scientific tools across protein research, antibody analysis, RNA design, and molecular dynamics:

• CANYA predicts protein nucleation propensity.

• ClinASO supports gapmer antisense oligonucleotide design and candidate ASO characterisation.

• P2PXML estimates antibody–antigen binding affinity.

• RFDpoly supports de novo design of DNA, RNA, proteins, and mixed assemblies.

• SF-Cluster generates MSA subsets for multi-conformer protein structure-prediction workflows.

• SSTMap analyses structural and thermodynamic properties of water around solute surfaces from molecular-dynamics trajectories.

• SuperMetal predicts zinc-binding-site locations in protein structures.

Together, these additions expand the computational options available for planning and running scientific workflows on Vecura.

Agent

Continue Agent chats with greater reliability

Agent chats now resume more reliably when a page reload interrupts a conversation.

Rendering improvements also prevent transient response content from appearing and disappearing unexpectedly. Opening the docked sidebar now begins a new conversation, creating a clearer starting point for a new line of inquiry.

Supporting more continuous research workflows

This week’s updates focus on helping researchers plan resource use, configure larger computational runs, resolve input issues earlier, and continue their work with fewer interruptions.

Follow What’s New on Vecura for regular updates on new models, scientific workflows, and platform capabilities.