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--- |
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license: apache-2.0 |
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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:" |
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extra_gated_fields: |
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First Name: text |
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Last Name: text |
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Company Name or Affiliation: text |
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Role or Job Title: text |
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I want to use AQAffinity for: text |
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tags: |
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- chemistry |
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- biology |
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- protein |
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- ligand |
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- binding |
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- affinity |
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- binding affinity |
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- drug discovery |
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--- |
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Introducing AQAffinity |
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============ |
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***SandboxAQ's open protein-ligand binding affinity prediction head built on top of OpenFold3*** |
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**In collboration with the OpenFold Consortium** |
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## Overview ## |
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This repository contains an implementation of a Binding Affinity Head designed to operate on top of the OpenFold3 architecture. |
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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. |
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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. |
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## Installation |
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### Prerequisites |
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This model requires `kalign2` for sequence alignment. Please install it using Mamba or Conda before installing the Python package: |
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```bash |
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mamba install kalign2 -c bioconda |
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``` |
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### Option 1: Direct Install (When publically released) |
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```bash |
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pip install git+[https://huggingface.co/SandboxAQ/aqaffinity](https://huggingface.co/SandboxAQ/aqaffinity) |
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``` |
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### Option 2: Local Install |
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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. |
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```bash |
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huggingface-cli login |
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hf download SandboxAQ/aqaffinity --local-dir ./aqaffinity |
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pip install ./aqaffinity |
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``` |
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## Running AQAffinity |
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```bash |
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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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``` |
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## Contributing to AQAffinity |
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We welcome contributions! This is a community effort. |
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Found a bug? Open an issue. |
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Have a better loss function? Submit a PR. |
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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. Upon the full public release (v1.0), this repository will include: |
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1. Manifests: Exact SQL commands used to apply filters to extract training and validation sets from the full Chembl, BindingDB, and Pubchem databases. |
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2. Preprocessing Scripts: The exact logic used to clean, perform post processing, and embedding generation for the train and validation sets. |
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3. Training Loop: PyTorch Lightning based training pipeline using cached embeddings. |
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Together, we can build a useful transparent instrument for scientific discovery. |
