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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the Above
Measures taken to mitigate against unwanted bias:	None of the Above

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Model Overview

Description:

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.

Third-Party Community Consideration

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.

License

DSMBind is provided under the Apache License 2.0.

References:

[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.

Model Architecture:

Architecture Type: Energy-Based Model (EBM)
\nNetwork Architecture: SE(3)-Invariant Neural Network

Input:

Input Type(s): Text (PDB, SDF)
\nInput Format(s): Protein Data Bank (PDB) Structure files for proteins, Structural Data Files (SDF) for ligands

Output:

Output Type(s): Numerical scores (indicating binding affinities)
\nOutput Format: List of scalar values
\nOther Properties Related to Output: Only the rank of the predicted values matters because the model produces comparative values instead of absolute binding energies.

Software Integration:

Runtime Engine(s):

BioNeMo (1.7), NeMo

Supported Hardware Microarchitecture Compatibility:

Ampere
Hopper

Preferred/Supported Operating System(s):

Linux

Model Version(s):

dsmbind.pth, version: 1.7

Training & Evaluation:

Training Dataset:

Link: a subset from PDB
\nData Collection Method by dataset

Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Our DSMBind checkpoint was trained using a subset of PDB. This subset includes a total of 25,561 samples, each representing a unique protein-ligand complex.

Evaluation Dataset:

Link: CASF-16
\nData Collection Method by dataset

Human
\nLabeling Method by dataset
Hybrid: Human & Automated
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): CASF-16 is an open challenge for comparative assessment of scoring functions. This benchmark has 285 protein-ligand complexes with binding affinity labels.

Inference:

Engine: BioNeMo, NeMo
\nTest Hardware:

Ampere

Evaluation Results

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.

Limitations

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.

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\n\n### Third-Party Community Consideration\n\nThis 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](https://github.com/wengong-jin/DSMBind).\n\n### License\n\nDSMBind is provided under the Apache License 2.0.\n\n### References:\n\n[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).
\n\n[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.
\n\n### Model Architecture:\n\n**Architecture Type:** Energy-Based Model (EBM)
\n**Network Architecture:** SE(3)-Invariant Neural Network
\n\n### Input:\n\n**Input Type(s):** Text (PDB, SDF)
\n**Input Format(s):** Protein Data Bank (PDB) Structure files for proteins, Structural Data Files (SDF) for ligands
\n\n### Output:\n\n**Output Type(s):** Numerical scores (indicating binding affinities)
\n**Output Format:** List of scalar values
\n**Other Properties Related to Output:** Only the rank of the predicted values matters because the model produces comparative values instead of absolute binding energies.
\n\n### Software Integration:\n\n**Runtime Engine(s):**\n* BioNeMo (1.7), NeMo
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* Ampere
\n* Hopper
\n\n**Preferred/Supported Operating System(s):**
\n* Linux
\n\n### Model Version(s):\n\ndsmbind.pth, version: 1.7\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n**Link:** a subset from [PDB](https://www.rcsb.org/)
\n**Data Collection Method by dataset**
\n* Human
\n **Properties (Quantity, Dataset Descriptions, Sensor(s)):** Our DSMBind checkpoint was trained using a subset of PDB. This subset includes a total of 25,561 samples, each representing a unique protein-ligand complex.
\n\n### Evaluation Dataset:\n\n**Link:** [CASF-16](http://www.pdbbind.org.cn/casf.php)
\n**Data Collection Method by dataset**
\n* Human
\n **Labeling Method by dataset**
\n* Hybrid: Human & Automated
\n **Properties (Quantity, Dataset Descriptions, Sensor(s)):** CASF-16 is an open challenge for comparative assessment of scoring functions. This benchmark has 285 protein-ligand complexes with binding affinity labels.
\n\n### Inference:\n\n**Engine:** BioNeMo, NeMo
\n**Test Hardware:**
\n* Ampere
\n\n### Evaluation Results\n\nWe 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.\n\n### Limitations\n\nDSMBind 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.","displayName","DSMBind","updatedDate",["D",1732745360699],"publisher","NVIDIA","latestVersion","1.7","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/models/dsmbind","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes",12272614,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above\nMeasures taken to mitigate against unwanted bias: | None of the Above","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------\nIntended Application(s) & Domain(s): | Protein-Ligand Binding Affinity Prediction\nModel Type: | Structural Biology for Drug Discovery\nIntended Users: | This model is intended for developers or researchers who want to study protein-ligand binding.\nOutputs: | A scalar value indicating the binding affinity\nDescribe how the model works: | This model takes a 3D protein-ligand complex structure as input and produces a scalar value with the learned model weights.\nTechnical Limitations: | This model produces only comparative values instead of absolute binding energy and may perform less well on non-crystal structures.\nVerified to have met prescribed NVIDIA standards: | Yes\nPerformance Metrics: | Pearson correlation between the prediction values and ground truth binding affinities\nPotential Known Risks: | Output value could lead to incorrect inference on binding affinity of a complex\nLicensing: | https://www.apache.org/licenses/LICENSE-2.0","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nProtected classes used to create this model? | Not Applicable\nWas consent obtained for any PII used? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No,not possible with externally sourced data\nApplicable NVIDIA Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:------------------------------------------------------------------\nModel Application(s): | Protein-Ligand Binding Affinity Prediction\nDescribe the life-critical application (if present).| None\nUse Case Restrictions: | Abide by https://docs.nvidia.com/ai-foundation-models-community-license.pdf\nDescribe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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These cookies may store a unique ID so that our system will remember you when you return. 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