| --- |
| license: apache-2.0 |
| extra_gated_prompt: "The AQAffinity preview model is released under Apache 2.0 license. You will automatically get access to the model after answering the following simple questions:" |
| extra_gated_fields: |
| First Name: text |
| Last Name: text |
| Company Name or Affiliation: text |
| Role or Job Title: text |
| I want to use AQAffinity for: text |
| tags: |
| - chemistry |
| - biology |
| - protein |
| - ligand |
| - binding |
| - affinity |
| - binding affinity |
| - drug discovery |
| --- |
| |
| Introducing AQAffinity |
| ============ |
| ***SandboxAQ's open protein-ligand binding affinity prediction head built on top of OpenFold3*** |
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| **In collaboration with the OpenFold Consortium** |
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| ## Overview ## |
| This repository contains an implementation of a Binding Affinity Head designed to operate on top of the OpenFold3 architecture. |
| It is a direct replication of the affinity prediction module introduced in Boltz-2 (by MIT/Recursion). |
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| The goal of this project is not to provide a final, closed commercial product, but to establish a strong, transparent baseline for the structural biology community. |
| We believe that binding affinity prediction, one of the "holy grails" of drug discovery, advances fastest when training data, pipelines, and model architectures are fully open for inspection and improvement. |
|
|
| ## Installation |
| ### Prerequisites |
| This model requires `kalign2` for sequence alignment. Please install it using Mamba or Conda before installing the Python package: |
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| ```bash |
| mamba install kalign2 -c bioconda |
| ``` |
|
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| ### Option 1: Direct Install |
| ```bash |
| pip install git+[https://huggingface.co/SandboxAQ/aqaffinity](https://huggingface.co/SandboxAQ/aqaffinity) |
| ``` |
|
|
| ### Option 2: Local Install |
| If the repository is private or you wish to download the source code first, use the Hugging Face CLI to download the repository and then install it locally. |
| ```bash |
| huggingface-cli login |
| hf download SandboxAQ/aqaffinity --local-dir ./aqaffinity |
| pip install ./aqaffinity |
| ``` |
| ## Running AQAffinity |
| ```bash |
| aqaffinity predict --query_json <of3_type_input_json> --runner_yaml <of3_runner_yaml> --inference_ckpt_path <of3_model_weights> --use_msa_server true --output_dir <output_dir> --binding_affinity_ckpt_path <binding_head_model_weights> |
| ``` |
|
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| ## Training AQAffinity |
| Training code is included and can be done by first generating embeddings using the ```predict``` mode with the ```--dump_embeddings flag``` on your training set and then ```aqaffinity train```, documentation and examples are currently being prepared |
| and will be available soon but please reach out if you need helping with training in the meantime. |
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| ## Contributing to AQAffinity |
| We welcome contributions! This is a community effort. |
| Found a bug? Open an issue. |
| Have a better loss function? Submit a PR. |
| Want to benchmark on a new dataset? Share your results in the Discussions tab. |
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| We believe "open source" means nothing without open data. Hence, we also release training and validation assay information we used to train and validate this model in csv file format. |
| User can use these to retrain and validate their own model. |
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| Both training and validation assay information was obtained following the procedures outlined in the official Boltz-2 preprint. |
| The corresponding SQL queries and preprocessing scripts will be added at a later point. |
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| Together, we can build a useful transparent instrument for scientific discovery. |
