DSMBind [1,2] is an energy-based model that has been trained on protein-ligand complexes to predict binding affinities. The model produces comparative values that are useful for ranking protein-ligand binding affinities. This model is for research and development only.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to the model card by Broad Institute of MIT and Harvard.
DSMBind is provided under the Apache License 2.0.
[1] Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, and Caroline Uhler. "Unsupervised protein-ligand binding energy prediction via neural euler's rotation equation." Advances in Neural Information Processing Systems 36 (2024).
[2] Wengong Jin, Xun Chen, Amrita Vetticaden, Siranush Sarzikova, Raktima Raychowdhury, Caroline Uhler, and Nir Hacohen. "DSMBind: SE (3) denoising score matching for unsupervised binding energy prediction and nanobody design." bioRxiv (2023): 2023-12.
Architecture Type: Energy-Based Model (EBM)
Network Architecture: SE(3)-Invariant Neural Network
Input Type(s): Text (PDB, SDF)
Input Format(s): Protein Data Bank (PDB) Structure files for proteins, Structural Data Files (SDF) for ligands
Output Type(s): Numerical scores (indicating binding affinities)
Output Format: List of scalar values
Other Properties Related to Output: Only the rank of the predicted values matters because the model produces comparative values instead of absolute binding energies.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Preferred/Supported Operating System(s):
dsmbind.pth, version: 1.7
Link: a subset from PDB
Data Collection Method by dataset
Link: CASF-16
Data Collection Method by dataset
Engine: BioNeMo, NeMo
Test Hardware:
We use gaussian noise to perturbe the ligand coordinates during training. We evaluate our trained DSMBind model on the CASF-16 benchmark. We measure the Pearson correlation coefficient to assess the linear relationship between the predicted scalar values and actual binding affinities. The trained checkpoint can achieve a Pearson correlation coefficient of 0.64.
DSMBind produces comparative values which are useful to rank complexes. But it does not provide absolute measures that are directly comparable to experimental ground truth affinities.