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)