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HighFold: A Generative Model for Structure Prediction
HighFold is a state-of-the-art, all-atom diffusion model for biomolecular structure prediction. It is a powerful foundation model that can predict the three-dimensional structures of proteins, nucleic acids, and small molecules, as well as complexes formed between them. HighFold has demonstrated exceptional performance on various benchmarks, with a high degree of accuracy and reliability.
A key feature of HighFold is its ability to handle diverse molecular systems from raw sequence and chemical inputs. It is particularly effective for predicting multimeric complexes and protein-ligand interactions. The model's predictions are highly precise, with HighFold achieving a success rate of 75% for predicting protein-ligand complex structures on the PoseBusters benchmark at a 2Å RMSD cutoff.
How HighFold Works
HighFold is a diffusion-based generative model that tackles the complex task of biomolecular structure prediction by learning to reverse a noisy process. The model's core architecture is a transformer-based neural network that uses an all-atom representation, meaning it considers every atom of the molecules.
The model works in two main phases:
The Forward Process (Noising): This process begins with a complete, all-atom structure of a biomolecule or complex. Over a series of steps, Gaussian noise is gradually added to the atomic coordinates. This effectively "destroys" the intricate structure of the molecule until it becomes a cloud of random points.
The Reverse Process (Denoising): This is the core of HighFold's generative power. The model is trained to learn the reverse of the noising process, meaning it can predict the noise that was added to a given state. By starting from a completely random cloud of atoms, HighFold iteratively removes the predicted noise, gradually refining the positions of every atom until it converges on a stable, physically realistic, and biologically accurate molecular structure.
A key component of the model is its ability to learn from a vast dataset of known structures from sources like the Protein Data Bank (PDB). This training allows HighFold to capture the underlying principles of biomolecular geometry and interactions, enabling it to generate highly accurate predictions for novel systems.
What is Tamarind Bio?
Tamarind Bio is a pioneering no-code bioinformatics platform built to democratize access to powerful computational tools for life scientists and researchers. Recognizing that many cutting-edge machine learning models are often difficult to deploy and use, Tamarind provides an intuitive, web-based environment that completely abstracts away the complexities of high-performance computing, software dependencies, and command-line interfaces.
The platform is designed provide easy access to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure but want to run experimental models with their data. Key features include a user-friendly graphical interface for setting up and launching experiments, a robust API for integration into existing research pipelines, and an automated system for managing and scaling computational resources. By handling the technical heavy lifting, Tamarind empowers researchers to concentrate on their scientific questions and accelerate the pace of discovery.
Accelerating Discovery with HighFold on Tamarind Bio
The integration of HighFold's advanced capabilities with Tamarind's user-centric platform creates a powerful synergy that can significantly accelerate the drug discovery and research process.
Unified Prediction and Flexibility: HighFold's ability to perform all-atom prediction for diverse molecules and complexes from a single model is a major advantage. On Tamarind, this is available through a unified, easy-to-use interface, allowing researchers to tackle a wide range of structural biology problems without switching tools.
Rapid and Accurate Insights: By abstracting away the computational and technical complexities, Tamarind allows researchers to run HighFold simulations at a massive scale, enabling them to quickly get high-quality predictions without the need for extensive computational setup.
Democratizing Structural Biology: The no-code interface and scalable cloud infrastructure make state-of-the-art tools like HighFold accessible to a broader scientific community, including researchers in academia and small biotech companies who might not have access to dedicated computational resources. This democratization of access to advanced AI models helps to accelerate fundamental research and drive innovation in therapeutic design.
How to Use HighFold on Tamarind Bio
Tamarind makes using HighFold straightforward and efficient, regardless of your technical expertise. The no-code platform streamlines the entire workflow for molecular structure prediction.
Here is a simple, step-by-step guide for researchers to get started:
Access the Platform: Log in to the tamarind.bio website.
Select HighFold: From the list of available computational models, choose the HighFold tool.
Specify Inputs: You can provide your input in various forms, such as protein sequences (FASTA), ligand chemical compositions (SMILES), or nucleic acid sequences.
Configure Parameters: In a simple, graphical user interface, you can specify your prediction parameters. You can choose to predict the structure of a single protein or a complex by providing multiple sequences and ligands.
Submit and Monitor: With a single click, you can submit your job. The Tamarind.bio platform handles the allocation of powerful GPU resources and executes the HighFold simulation. You can monitor the progress of your job directly from a user-friendly dashboard.
Analyze the Results: Once the job is complete, you will receive a comprehensive report with the predicted structures. You can explore interactive 3D visualizations of the structures directly in your browser, enabling you to inspect the predicted poses and their overall quality. The platform also ensures that your inputs and outputs are securely stored in your private cloud, and that you retain ownership of all your data.