Use FlowDock

Official Neurosnap webserver for accessing FlowDock online.

Overview

FlowDock is a generative framework for protein-ligand docking and binding affinity estimation. Leveraging geometric flow matching, it enables accurate predictions of protein-ligand complex structures and their binding affinities, supporting flexible docking for multiple ligands simultaneously. FlowDock incorporates state-of-the-art techniques like ESMFold and harmonic ligand priors to map unbound (apo) structures to their bound (holo) counterparts, providing confidence scores and affinity predictions for virtual screening. Benchmark results highlight its efficacy on datasets like PoseBusters and DockGen-E, achieving competitive docking success rates and top-5 affinity prediction performance in the CASP16 competition.

Neurosnap Overview

The FlowDock online webserver allows anybody with a Neurosnap account to run and access FlowDock, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

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Features

  • Integrates geometric flow matching to predict protein-ligand structures and binding affinities.
  • Supports flexible docking for multiple ligands simultaneously.
  • Achieves state-of-the-art performance in docking success rates on challenging datasets like PoseBusters and DockGen-E.
  • Uses ESMFold-based protein priors and harmonic ligand priors for accurate apo-to-holo mappings.

Statistics

Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.

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API Request

Access FlowDock using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

The amino acid sequence(s) corresponding to the chains of the receptor protein.

An optional already docked protein-ligand(s) complex.

Input small molecules for docking. All inputs are converted to SMILES format, with SMILES strings being the preferred input for optimal results.

The number of samples to generate.

Number of sampling steps to perform for each sample.

Advanced Settings

FlowDock employs both Ordinary Differential Equation (ODE) and Variance Diminishing ODE (VD-ODE) solvers to integrate its learned vector fields during the docking process, with VD-ODE being the default. The ODE solver ensures smooth and accurate trajectories between the unbound (apo) and bound (holo) states, maintaining consistency with the model's learned flow field. In contrast, the VD-ODE solver introduces variance damping during early timesteps, sharply interpolating toward the final prediction in later stages for greater stability and efficiency.

The Variance Diminishing ODE (VD-ODE) sampler includes a variance diminishing factor that controls the balance between exploration and exploitation during the sampling process. This factor offers a trade-off, with a value of 1.0 encouraging broader sampling to explore diverse structural conformations, while values greater than 1.0 prioritize exploitation, focusing on refining specific predictions and converging on high-confidence solutions. The default value is 1.0, ensuring a balanced approach suitable for most docking scenarios.

FlowDock uses two main priors to initialize unbound (apo) structures: the ESMFold prior for proteins and the harmonic prior for ligands. The ESMFold prior, which is the default for proteins, predicts apo structures directly from their sequences using the ESMFold model and adds a small amount of Gaussian noise to prevent overfitting and capture natural variations. The harmonic prior generates initial ligand conformations based on the ligand's bond graph, ensuring physically plausible starting structures. These priors provide reliable and biologically meaningful foundations for mapping apo structures to their bound (holo) states during docking.

Whether to use the ground-truth binding site for sampling, if it is available.

Whether to detect covalent bonds between the input receptor and ligand, if present.

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