https://boltz.bio/boltzgen

Now an embeddable, shareable, editable web workflow

With metapage web workflows, we can embed AI tools directly in the browser in an open-source framework, and immediately share with others.

The embedded workflow below only shows a portion of the full workflow, click on the pencil icon on the right to see the full workflow

https://metapage.io/m/a8e6dd78d176472098add0833ba5d76a

What’s happening here?

Metapages are workflows (visualization and containers) built for the web, sharing and publishing. Boltzgen is a new AI model for generating molecular structures for binding, and we have containerized boltzgen in a web format.

This means you can experiment with it immediately, and quickly add your own analyses and visualizations (just clone the metapage).

Where is compute done? On our public compute grid.

I was lucky to attend the conference Finding Your Inner Modeler: “The Future of Mechanistic Modeling in the Age of AI”.

These are the people the metapage platform is aimed at: those solving hard, complex problems at the intersection of data, math, visualization, who need better tools to communicate and collaborate.

But it’s hard to share the code and workflows in a way where you can speak in models and simulations

Speed

During two talks, I looked up the code respositories, and containerized the code, added basic visualization (if it was simple to do), and made the workflows public, availalble for anyone to quickly get started, or incorporate the code immediately to their own workflows.

Each took about 15 minutes, and that’s time that no-one else now has to pay, and these are now available to everyone, forever.

MEDYAN - Mechanochemical Dynamics of Active Networks

The workflow:

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

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Every week on Linkedin and other social media there is a new exciting generative model or molecular simulation or binding prediction tool released (click here to see just my stream). Exciting times!

It’s hard to keep up! New tools require testing and evaluation, and while we can work to automate a lot of the evaluation, at some point humans need to experiment with and test these tools in their own workflows.

Competitions: complex tools require social-technical collective evaluation

Academic and industry groups have used competitions to leverage our collective focus. They bring people together with both competitive fun, and collective learning. Some recent examples:

Public competitions are an excellent way to learn from each other and channel resources. Bits to Binders was another one of these kinds new challenges/competitions, and I threw my hat in the ring!

Myself and 4 others joined this challenge to form a team that would generate a protein binder to a specific immune cell receptor, where a binder that would bind just right (not too much, not too little) would have the potential to treat some specific cancers.

Our team had access to GPU resources, the target, and a brand new generative AI model for protein design: RFDiffusion. None of us previously knew each other, and all come with different backgrounds and experience and technology

However, despite our enthusiasm, our team, and many like ours, encountered significant struggles. Internally within group, we struggled to effectively share workflows and results. The organizers of the competition had done a great job to gather sponsors and organizations who collectively provided a significant number of valuable GPU resources.

We could only utilize a very tiny fraction of available GPUs due to both mundane and complex technical and logistical challenges. There was simply a lack of infrastructure to support this social-technical phenomenon. We wanted a vibrant community, and the organizers did an absolutely fantastic job, but the outcome of the competition specifically in respect to sharing the methodologies was hampered by a lack of suitable infrastructure.

Put simply, there is no universal, straightforward way to share a computational workflow that requires visualization and interactivity (due to molecular structures) also connected to GPUs running AI models and simulations, running seamlessly on whatever GPU resources are available to you. While workflow systems like Nextflow and others can be a piece of the puzzle, they lack specific features around sharing and publishing due to the inherent technical tradeoffs.

This missing connection means that innovation remained isolated in the groups and labs of origin, regardless of the desire to share them.

What about the competition leaderboard examples you posted

The Adaptyv Bio competition website looks fantastic, significant work and thought went into the design and infrastructure:

And that is part of the problem. Though well-designed, the competition site is a bespoke presentation, often not containing the methods, and results that are difficult to incorporate or evaluate systematically, and in the example above, there are no methods for you to copy and test yourself.

For the ASAP Discovery competition, for some entries there is code, but the </> code link is simply to a github repository. That means a lot of custom, bespoke work to understand, and possibly use those methods:

So in summary: you often cannot get methods and tools, and if you can the code is in a form that requires significant work just to run yourself. It’s too slow and limited, and this limits our ability to share and learn from each other, and it wastes a lot of repeated work, as everyone who wants to use a given tool has to do the same work (wasting significant effort) getting it working.

MESH (Molecular Evaluation Science Hub)

Recognizing these limitations, we propose a socio-technical initiative: MESH. Using some recent breakthroughs in publishing scientific workflows, we aim to harness the very best aspects of human collaboration: shared innovation and collective learning, and in this particular space, via leaderboards of workflows. These workflows are run and expressed in the browser, even if they are only headless workflows. The workflows can be shared, modified, and run entirely in the browser, even though there are backend containers running on some grid or cluster.

A central part of our vision is developing the collective set of open-source tools that express workflows directly in the browser: metapage workflows, combined with existing proven social structures for efficient idea exploration and refinement: public competitions and leaderboards that can rapidly and efficiently evaluate and spread to target communities novel and powerful molecular dynamics software tools, in a form they can immediately use.

The metapages platform and tools allow researchers to easily create persistent, durable, yet flexible leaderboards. These leaderboards aren't just static records; they are dynamic, adaptable workflows that researchers can easily manipulate, extend, and incorporate into their own workflows, in whole or in by parts. By leveraging a public compute grid, researchers can effortlessly connect their own or others computational resources, whether their own laptop, HPC clusters, or turn-key cloud solutions. This makes shared workflows independent of where they run (as long as you have or we provide the necessary compute resources), thus enhancing accessibility and reducing the barriers to participation and collaboration.

Example workflow in the browser

Here is an example that was quickly put together to test a tool, shared among the team, that we can share with the entire world simply via a url or embedding anwhere where URLs can be: https://metapage.io/m/71990da894774ebca5a4ad2895201a5c

The outputs of this workflow tool can be connected to other workflows, and copied with a single click, replicating all the code and data.

This initiative is in the house of The Open Molecular Software Foundation whose mission statement is a succinct superset of ours. Our particular alignment is especially about accelerating progress. We envision empowering researchers of all technical backgrounds to seamlessly integrate innovations in molecular dynamics AI models developed by one group into the workflows of another, and they will be able to do this because they will be able to rely on tools that are more reliable, as the tools will have been (and can continue to be evaluated by a community. This democratization of AI tools and knowledge will unlock previously constrained collaboration potential, fueling advancements and discoveries across the molecular biology and broader scientific community.

We will follow up with a post about the technical differences between metapage workflows and other comparable stacks such as Jupyter Notebooks and workflow systems such as Nextflow.

References

Example docking workflow

Competition with dashboard

https://foundry.adaptyvbio.com/competition

https://beta.adaptyvbio.com/benchbb

https://polarishub.io/blog/antiviral-competition

https://paperswithcode.com/method/clip

🎯 The Challenge​

Using the Metapages platform, create an interactive visualization workflow that explores differential gene expression patterns. Your creation should help researchers understand expression differences between conditions (e.g., disease vs. healthy, treated vs. untreated, developmental stages) while providing an intuitive, interactive interface.

📊 Dataset Guidelines​

You can use any gene expression dataset of your choice. Your dataset should:

• Contain gene expression measurements

• Be publicly available or properly licensed for use

• Include sufficient samples for meaningful visualization (typically >10 samples)

💡 Example Data Sources​

If you're looking for a place to start, here are some suggested resources:

1. Gene Expression Omnibus (GEO)

   • Vast repository of expression datasets

   • Many cancer and disease studies

   • Various organism and tissue types

   • https://www.ncbi.nlm.nih.gov/geo/

2. The Cancer Genome Atlas (TCGA)

   • Comprehensive cancer genomics data

   • Multiple cancer types

   • Rich clinical metadata

https://portal.gdc.cancer.gov/

1. Example Starter Dataset

   • COVID-19 patient transcriptomics (GSE152075)

📋 Suggested Visualizations​

Your workflow could include:

• Interactive heatmaps

• Volcano plots of differential expression

• PCA or t-SNE dimensionality reduction plots

• Gene expression boxplots

• Custom visualizations of your design

🎨 Evaluation Criteria​

Submissions will be judged on:

• Technical Implementation: Effective use of Metapages features

• Visual Design: Clear, intuitive, and aesthetically pleasing

• Biological Insight: Meaningful exploration of the data

• Interactivity: User-friendly interface and meaningful interactions

• Innovation: Creative approaches to visualization

🏆 Prizes​

• First Place:

  • $200 Amazon Gift Card

  • Featured spotlight on Metapages homepage

  • Recognition in Metapages community showcase

📅 Timeline​

• Start Date: Thurday, February 20, 2025

• Submission Deadline: Wednesday, February 26, 2025 (11:59 PM GMT)

• Winners Announced: Thursday, February 27, 2025

📫 How to Submit​

1. Create your workflow on the Metapages platform:

https://metapage.io

1. Ensure your workflow is publicly accessible

2. Submit to competition@metapage.io including:

   • Your workflow's public URL

   • Your name

   • Optional:

     • LinkedIn Profile (encouraged)

     • Brief description of your visualization approach (max 300 words)

     • Technical implementation details

     • Platform feedback

🚀 Getting Started​

Visit our Quick Start Guide for:

• Dataset loading examples

• Basic visualization templates

• Component communication tutorials

• Bioinformatics workflow examples

🛠️ Important Notes​

• The platform is in alpha - reach out if you encounter any issues

• Focus on creating clear, intuitive visualizations

• No prior bioinformatics experience required

• We encourage creative approaches!

❓ Questions?​

• Join our Discord community for technical support

• Email support@metapage.io for platform-specific questions

• Connect on Bluesky for general discussion

Good luck! We're excited to see your creative approaches to biological data visualization! 🧬 ✨

In the example below, there is an embedded protein visualization. When you click the link, you have all the code to run this yourself, and you can edit it directly in the browser, and instantly share your changed version with anyone.

To help spread awareness, we are having a series of competitions to see what visualizations and workflows member of the science and data science community come up with.

https://docs.metapage.io/blog/competition-1-2

Our goal is to upgrade all researchers and data scientists ability to communicate, by making complex code, data, and visualization be sent instantly, and reliably run no matter how old, and to be able to take any part of anyone else’s workflow or visualization, and incorporate it into your own projects in a matter of seconds.

Spread the word! We aim to be as open (source and science) as possible, bootstrapping to avoid VC funding.