data_summary_card.md

 

  

 

# Data Summary for Graphormer 

 

This data summary was created by Microsoft on behalf of the model developer and may contain mistakes 

 

## 1. General information 

**1.0.1 Version of the Summary:** 1.0 

 

**1.0.2 Last update:** 21-Nov-2025

 

## 1.1 Model Developer Identification 

**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080. 

 

## 1.2 Model Identification 

**1.2.1 Versioned model name(s):** Graphormer 

 

**1.2.2 Model release date:** 20-Aug-2024 

 

## 1.3 Overall training data size and characteristics 

### 1.3.1 Size of dataset and characteristics 

**1.3.1.A Text training data size:** Not applicable. Text data is not part of the training data 

 

 

**1.3.1.B Text training data content:** Not applicable 

 

**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data 

 

**1.3.1.D Image training data content:** Not applicable 

 

**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data 

 

**1.3.1.F Audio training data content:** Not applicable 

 

**1.3.1.G Video training data size:** Not applicable 

 

**1.3.1.H Video training data content:** Not applicable. Video data is not part of the training data 

 

**1.3.1.I Other training data size:** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 

**1.3.1.J Other training data content:** Structures from the Protein Data Bank (PDB) were used for training and as templates (https://www.wwpdb.org/ftp/pdb-ftp-sites; for the associated sequence data and 100% sequence clustering see also https://ftp.wwpdb.org/pub/pdb/derived_data/and https://cdn.rcsb.org/resources/sequence/clusters/clusters-by-entity-100.txt). Training used a version of the PDB downloaded on 25 December 2020. The template search also used the PDB70 database, downloaded 13 May 2020 (https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/). For MSA lookup at both the training and prediction time, Uniclust30 v.2018_08 (https://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/) were used. The milisecond MD simulation trajectories for the RBD and main protease of SARS-CoV-2 are downloaded from the coronavirus disease 2019 simulation database (https://covid.molssi.org/simulations/). 238 simulation trajectories from the GPCRmd dataset (https://www.gpcrmd.org/dynadb/datasets/) are also included. Protein–ligand docked complexes are collected from CrossDocked2020 dataset v1.3 (https://github.com/gnina/models/tree/master/data/CrossDocked2020). The OC20 dataset was also used for catalyst-adsoprtion generation modelling (https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md).  

 

**1.3.2 Latest date of data acquisition/collection for model training:** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 

**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No 

 

**1.3.4 Date the training dataset was first used to train the model:** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 

**1.3.5 Rationale or purpose of data selection:** The equilibrium distribution of protein conformations is difficult to obtain, leading to limited high quality data for training or benchmarking. Experimental and simulated structures were collected from public databases as a starting point. This was supplemented with simulated data to mitigate data scarcity. 

 

## 2. List of data sources 

### 2.1 Publicly available datasets 

**2.1.1 Have you used publicly available datasets to train the model?** Yes 

 

## 2.2 Private non-publicly available datasets obtained from third parties 

### 2.2.1 Datasets commercially licensed by rights holders or their representatives 

**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 

### 2.2.2 Private datasets obtained from other third-parties 

**2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 

## 2.3 Personal Information 

**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information. 

 

## 2.4 Synthetic data 

**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes 

 

## 3. Data processing aspects 

### 3.1 Respect of reservation of rights from text and data mining exception or limitation 

**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent. 

 

## 3.2 Other information 

**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities. 

 

**3.2.2 Was the dataset cleaned or modified before model training?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)