DiffDock Now Implemented In Boltzmann Maps

DiffDock Diffusion-Based AI Model Available for Quick Protein-Ligand Docking in Boltzmann Maps

Seeking to democratize the latest tooling for computational drug discovery, the Boltzmann Maps team is proud to announce the integration of the AI model DiffDock [1]. In comparison on the PDBBind ligand docking task, DiffDock achieved a 38% top-1 success rate for binding ligands within 2A RMSD of the crystal docking site. This outperformed traditional Glide docking at 23% and other leading deep learning methods at 20% [1].Furthermore, docking runs take less than a minute for most protein-ligand combinations, transforming the possibilities for traditional computational workflows.

Visualization of DiffDock results in BMaps


Now available in BMaps as an additional option alongside our previously implemented AutoDock Vina capabilities, DiffDock in Boltzmann Maps now supports:

  • Fast rigid, full-protein docking of ligands
  • Visualization of up to the 10 best poses for each docked compound
  • Energy minimization and scoring of docked poses with calculated physiochemical properties.

Use DiffDock Today!

Freely play around with DiffDock today:

  1. Log into Boltzmann Maps
  2. Bring in a protein and a compound
  3. Use options in the compound menu to dock.

Then, you can follow up your docking runs of multiple compounds by minimizing the docked geometries and calculating the energy score for each pose to discover which compound binds best to your protein of interest! Additionally, you can compare results from AutoDock Vina and hot spot analysis side by side to gain confidence in your results through the use of lateral methods.

Next Steps

This is the first post in a series of blogs on DiffDock.
See the next blog post for a full tutorial on using this tool in BMaps.

For greater detail about the theory and implementation of DiffDock, we recommend the original publication and associated Github. Stay tuned for an upcoming blog post breaking down this theory.

[1] Corso, Gabriele, et al. “Diffdock: Diffusion steps, twists, and turns for molecular docking.” arXiv preprint arXiv:2210.01776(2022).

Need help with your computational chemistry and biology tasks? Conifer Point, maker of Boltzmann Maps, proudly offers CRO services.

Alphafold AI-generated protein structure to empower Boltzmann Maps Fragment-based Drug Design

Since its first public test in 2018, Alphafold has made great strides in providing the scientific community with highly accurate AI-generated protein structure predictions. A recent database release by Alphafold contained over 200 million entries and boasts broad coverage of the UniProt protein sequence and annotation repository. Boltzmann Maps puts the power of the Alphafold database directly in the hands of users and allows for advanced analysis of protein structures.

Continue reading Alphafold AI-generated protein structure to empower Boltzmann Maps Fragment-based Drug Design

Pharmacophore screening using Pharmit in Boltzmann Maps

Boltzmann Maps is pleased to introduce an integration with Pharmit as an option for pharmacophore screening. Pharmit is a search tool for finding small molecule inhibitors that bind to a target of interest. The tool searches libraries for compounds with desired features in the right geometry. Boltzmann Maps integration allows the user to send a protein-ligand system from BMaps to Pharmit for search based on the compound’s features or other user-specified features. Pharmit’s nine built-in libraries include almost 250M compound entries, and the 1,059 publicly accessible user-contributed libraries contain another 45M entries.

Pharmit can be accessed via the Export button on the bottom right of the BMaps web app.

Continue reading Pharmacophore screening using Pharmit in Boltzmann Maps

100% PDB Availability and Automation of Protein Preparation

With the new release of Boltzmann Maps comes enhanced reliability for protein structure loading and automation of protein preparation for energy minimization, docking and fragment simulations. The entirety of the Protein Data Bank (PDB) is now available to view in Boltzmann Maps. 

As an example, log into BMaps to view PDB ID 3n7h: https://www.boltzmannmaps.com/structure/3n7h. The PDB featured this mosquito odorant binding protein in complex with DEET (DE3 ligand) as one of the “Molecules of the Month” for June 2023.

Continue reading 100% PDB Availability and Automation of Protein Preparation

Compound Energy Minimization with OpenMM

The Boltzmann Maps web app now employs GPU-accelerated OpenMM software for compound energy minimization in the context of a protein. The reported energies include van der Waals and electrostatic energies between compound and protein, as well as the change of a compound’s internal energies between the unbound and bound configurations (stress). These energy reports are a key metric for evaluating and comparing compounds and modifications. OpenMM integration allows Boltzmann Maps to provide this data with improved quality and speed.

OpenMM is an open-source toolkit for molecular simulation. It is highly flexible with its custom functions and has high performance, especially on recent GPUs. More information can be found at: https://openmm.org.

Continue reading Compound Energy Minimization with OpenMM

Starting structure-based design with sample compounds

Structure-based drug design starts with a compound positioned on the surface of a protein. Often, the crystal ligand in a PDB structure provides the natural starting point. But what if there is no ligand? This question took on increased urgency as the Boltzmann Maps team prepared fragment simulations on COVID-19 structures; many of the initial entries in the PDB did not have ligands. In response to this, Boltzmann Maps now provides Sample Compounds. We docked 20K+ small molecules from our libraries against hotspots on each structure and selected a handful to show in BMaps. The resulting compounds are generally commercially available, chemically diverse, and are reasonable starting points for structure-based design. They can then be used to explore new designs, using BMaps’ energy analysis and fragment data.

Sample Compounds are available for almost all of the SARS-CoV-2 structures in Boltzmann Maps. More are coming!

View a COVID-19 structure with sample compounds now >>. Or, log in to start exploring your own structure-based design modifications.

Continue reading Starting structure-based design with sample compounds

New: Fragment maps for 10 SARS-CoV-2 proteins

Fragment maps for ten SARS-CoV-2 virus proteins are now available in the BMaps web app to accelerate the design of COVID-19 therapeutics. These structures include the main protein protease (NSP5), the Spike protein (S), the receptor binding domain (RDB) of the S protein, and several NS (non-structural) proteins NSP3, NSP9, NSP10, NSP15, NSP16. Available for each protein are druggability sites, water molecule maps, and a starting set of 117 chemical fragment binding maps.

Continue reading New: Fragment maps for 10 SARS-CoV-2 proteins

Fragment Maps for Coronavirus (6LU7) Available

Start designing your COVID-19 protease inhibitors using BMaps with an expanded set of 221 fragments. The example below started with a benzimidazole fragment, then grew to a benzene-CF3 via intermediate linkers. To get started with your own possibilities, view the BMaps prepared 6LU7 structure. And stay tuned for more coronavirus structures currently in BMaps fragment simulation (6LVN, 6VSB).

To learn more about the coronavirus, visit https://www.cdc.gov/coronavirus/2019-ncov/ or https://en.wikipedia.org/wiki/Coronavirus_disease_2019.

Image of an example coronavirus protease inhibitor, assembled from Boltzmann Maps simulated fragment maps.
CoV-2 Structures

Latest: More fragment grow results & “Rings-in-Drugs”

The latest BMaps update has support for a broader set of fragment linking options when growing with fragments, significantly expanding the opportunities for compound modifications. By accessing more fragments in new sub-pockets, the new linking features provide lots of new ideas for improvements to your compounds, ranked by fragment binding scores. In addition to simple bond linking, there is now:

  • methylene linking (–C– methane or single carbon);
  • ethane linking (–C–C– 2 carbons);
  • acetylene linking (–C≡C– 2 carbons connected by a triple bond).

Continue reading Latest: More fragment grow results & “Rings-in-Drugs”

New: Fragment map summaries and more fragment libraries

The latest Boltzmann Maps enhancements make it easier to apply insights from fragment data to chemical design.

BMaps now includes visualization of fragment map summaries for each fragment map available for a given protein. Map summaries show the highest affinity (lowest chemical potential) pose of a fragment at each site on a protein where the fragment binds.  This information is useful in identifying promising scaffold fragments or new sub-pockets to exploit.

Continue reading New: Fragment map summaries and more fragment libraries