npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@datagrok/bionemo

v1.0.0

Published

Advanced models for protein structure prediction and molecular docking

Downloads

66

Readme

BioNeMo

The BioNeMo package integrates advanced models for protein structure prediction and molecular docking. It features EsmFold, which predicts 3D protein structures from amino acid sequences, and DiffDock, which predicts molecular interactions with proteins.

EsmFold model

EsmFold predicts 3D structures of proteins based on their amino acid sequences.

To apply EsmFold to an entire column:

  • Navigate to the top menu and select Bio > BioNemo > EsmFold.
  • In the dialog:
    • Choose the dataframe: Select the dataframe containing your sequences.
    • Select the column with sequences: Choose the column that contains the amino acid sequences.
  • Click OK to process the sequences.

EsmFold will generate 3D structure predictions for all sequences in the selected column and save them in a new column.

esmfold for column

To use EsmFold for a single sequence:

  • Select the specific sequence in your dataset.
  • The EsmFold panel will appear, showing the 3D structure prediction for that sequence.

esmfold for sequence

DiffDock model

DiffDock predicts molecular interactions with proteins by generating 3D poses of these interactions.

To apply DiffDock to an entire column:

  • Navigate to the top menu and select Chem > BioNemo > DiffDock.
  • In the dialog:
    • Choose the dataframe: Select the dataframe with your data.
    • Specify the column with ligands: Choose the column containing the ligands for docking.
    • Select the target: Choose the protein target for the interaction.
    • Set the number of poses: Specify how many poses DiffDock should generate for each ligand.
  • Click OK to start the process.

DiffDock will generate multiple poses for each ligand, identify the best one, and add them along with confidence values to a new column.

To view generated poses:

  • Click on a pose in the dataset. The Mol* viewer will open to display the selected pose.
  • In the Mol* viewer, use the combo popup to see additional poses and their confidence levels. Select a pose to view it.

diffdock for column

To use DiffDock for a single cell:

  • Select the structure in your dataset. The DiffDock panel will appear.
  • Specify the target and number of poses.
  • The Mol* viewer will show the generated poses and their confidence values.