File size: 5,148 Bytes
f68ffd1 7fa444c f68ffd1 7fa444c f68ffd1 726f996 f68ffd1 7fa444c f68ffd1 7fa444c f68ffd1 726f996 f68ffd1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
---
license: apache-2.0
metrics:
- accuracy
pipeline_tag: feature-extraction
tags:
- chemistry
- foundation models
- AI4Science
- materials
- molecules
- safetensors
- pytorch
- transformer
- diffusers
library_name: transformers
---
# SMILES-based State-Space Encoder-Decoder (SMI-SSED) - MoLMamba
This repository provides PyTorch source code associated with our publication, "A Mamba-Based Foundation Model for Chemistry".
**Paper NeurIPS AI4Mat 2024:** [Arxiv Link](https://openreview.net/pdf?id=HTgCs0KSTl)
For more information contact: [email protected] or [email protected].
## Introduction
We present a Mamba-based encoder-decoder chemical foundation model, SMILES-based State-Space Encoder-Decoder (SMI-SSED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-SSED supports various complex tasks, including quantum property prediction, with two main variants ($336$ and $8 \times 336M$). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks.
We provide the model weights in two formats:
- PyTorch (`.pt`): [smi_ssed_130.pt](smi_ssed_130.pt)
- safetensors (`.bin`): [smi_ssed_130.bin](smi_ssed_130.bin)
For more information contact: [email protected] or [email protected].
## Table of Contents
1. [Getting Started](#getting-started)
1. [Pretrained Models and Training Logs](#pretrained-models-and-training-logs)
2. [Replicating Conda Environment](#replicating-conda-environment)
2. [Pretraining](#pretraining)
3. [Finetuning](#finetuning)
4. [Feature Extraction](#feature-extraction)
## Getting Started
**This code and environment have been tested on Nvidia V100s and Nvidia A100s**
### Pretrained Models and Training Logs
We provide checkpoints of the SMI-SSED model pre-trained on a dataset of ~91M molecules curated from PubChem. The pre-trained model shows competitive performance on classification and regression benchmarks from MoleculeNet.
Add the SMI-SSED `pre-trained weights.pt` to the `inference/` or `finetune/` directory according to your needs. The directory structure should look like the following:
```
inference/
├── smi_ssed
│ ├── smi_ssed.pt
│ ├── bert_vocab_curated.txt
│ └── load.py
```
and/or:
```
finetune/
├── smi_ssed
│ ├── smi_ssed.pt
│ ├── bert_vocab_curated.txt
│ └── load.py
```
### Replicating Conda Environment
Follow these steps to replicate our Conda environment and install the necessary libraries:
#### Create and Activate Conda Environment
```
conda create --name smi-ssed-env python=3.10
conda activate smi-ssed-env
```
#### Install Packages with Conda
```
conda install pytorch=2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia
```
#### Install Packages with Pip
```
pip install -r requirements.txt
```
## Pretraining
For pretraining, we use two strategies: the masked language model method to train the encoder part and an encoder-decoder strategy to refine SMILES reconstruction and improve the generated latent space.
SMI-SSED is pre-trained on canonicalized and curated 91M SMILES from PubChem with the following constraints:
- Compounds are filtered to a maximum length of 202 tokens during preprocessing.
- A 95/5/0 split is used for encoder training, with 5% of the data for decoder pretraining.
- A 100/0/0 split is also used to train the encoder and decoder directly, enhancing model performance.
The pretraining code provides examples of data processing and model training on a smaller dataset, requiring 8 A100 GPUs.
To pre-train the SMI-SSED model, run:
```
bash training/run_model_training.sh
```
Use `train_model_D.py` to train only the decoder or `train_model_ED.py` to train both the encoder and decoder.
## Finetuning
The finetuning datasets and environment can be found in the [finetune](finetune/) directory. After setting up the environment, you can run a finetuning task with:
```
bash finetune/smi_ssed/esol/run_finetune_esol.sh
```
Finetuning training/checkpointing resources will be available in directories named `checkpoint_<measure_name>`.
## Feature Extraction
The example notebook [smi_ssed_encoder_decoder_example.ipynb](notebooks/smi_ssed_encoder_decoder_example.ipynb) contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks. It also includes examples of classification and regression tasks. For model weights: [HuggingFace Link](https://huggingface.co/ibm/materials.smi-ted)
To load smi-ssed, you can simply use:
```python
model = load_smi_ssed(
folder='../inference/smi_ssed',
ckpt_filename='smi_ssed.pt'
)
```
To encode SMILES into embeddings, you can use:
```python
with torch.no_grad():
encoded_embeddings = model.encode(df['SMILES'], return_torch=True)
```
For decoder, you can use the function, so you can return from embeddings to SMILES strings:
```python
with torch.no_grad():
decoded_smiles = model.decode(encoded_embeddings)
```
|