Publicly Available Models§
Below an overview of our available open-source models. Find them on GitHub here.
Go to Deploying Models for more detailed deployment options beyond the quick-start.
SMI-TED / Inference for SMILES§
openad-service-smi-ted compose.yml Instructions
This OpenAD service provides access to the SMILES-based Transformer Encoder-Decoder (SMILES-TED), which is an encoder-decoder model pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants ( 289 M and 8 × 289 M ).
More information:
github.com/IBM/materials
huggingface.co/ibm/materials.smi-ted
arxiv.org/abs/2407.20267
Support for:
✅ Docker / Podman Compose
✅ Docker / Podman
✅ Apple Silicon - more info
Quick start with Docker Compose:
BMFM-SM / Inference for SMILES§
openad-service-bmfm-sm compose.yml Instructions
This OpenAD service provides access to the Biomedmultiview foundation model with checkpoints for the following properties:
| BACE | BBBP | CLINTOX | ESOL | FREESOLV | HIV |
|---|---|---|---|---|---|
| LIPOPHILICITY | MUV | QM7 | SIDER | TOX21 | TOXCAST |
|---|---|---|---|---|---|
More information:
github.com/BiomedSciAI/biomed-multi-view
arxiv.org/abs/2410.19704
Support for:
✅ Docker / Podman Compose
✅ Docker / Podman
✅ Apple Silicon - more info
Quick start with Docker Compose:
BMFM-PM / Inference for Proteins using MAMMAL§
openad-service-bmfm-pm compose.yml Instructions
This OpenAD service provides access to the Biomedmultialignment foundation model with checkpoints for the following properties:
| Sol | DTI |
|---|---|
More information:
github.com/BiomedSciAI/biomed-multi-alignment
Support for:
✅ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker Compose:
REINVENT 4 / Inference for SMILES§
openad-service-reinvent4 compose.yml Instructions
This OpenAD service provides access to the REINVENT 4 molecular design tool, which is used for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. REINVENT uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.
More information:
github.com/MolecularAI/REINVENT4
link.springer.com/article/10.1186/s13321-024-00812-5
Support for:
✅ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker Compose:
Generation Inference for SMILES§
openad-service-gen Instructions
No description available.
Support for:
❌ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker:
Property Inference for SMILES§
openad-service-prop Instructions
No description available.
Support for:
❌ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker:
MOLER Inference for SMILES§
openad-service-moler Instructions
No description available.
Support for:
❌ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker:
MoLFormer Inference for SMILES§
openad-service-molf Instructions
No description available.
Support for:
❌ Docker / Podman Compose
✅ Docker / Podman
❌ Apple Silicon - more info
Quick start with Docker: