lchemme 0.0.3

Training and applying large chemistry models for embeddings.

🚄 lchemme

Pretraining large chemistry models for embedding.

• Installation
• Command-line usage
  • Example
  • Other commands
• Python API
• Documentation

Installation

The easy way

Install the pre-compiled version from PyPI:

pip install lchemme

From source

Clone the repository, then cd into it. Then run:

pip install -e .

Command-line usage

lchemme provides command-line utlities to pre-train BART models.

To get a list of commands (tools), do

$ lchemme --help
usage: lchemme [-h] [--version] {tokenize,pretrain,featurize} ...

Training and applying large chemistry models.

options:
  -h, --help            show this help message and exit
  --version, -v         show program's version number and exit

Sub-commands:
  {tokenize,pretrain,featurize}
                        Use these commands to specify the tool you want to use.
    tokenize            Tokenize the data inputs.
    pretrain            Pre-train a large language model using self-supervised learning.
    featurize           Get vector embeddings of a chemical dataset using a pre-trained large language model.

And to get help for a specific command, do

$ lchemme <command> --help

Tokenizing

The first step is to build a tokenizer for your dataset. LChemME works with BART models, and pulls their architecture from the Hugging Face Hub or a local directory. Training data can also be pulled from Hugging Face Hub with the hf:// prefix, or it can be loaded from local CSV files with a column containing SMILES strings.

lchemme tokenize \
    --train hf://scbirlab/fang-2023-biogen-adme@scaffold-split:train \
    --column smiles \
    --model facebook/bart-base \
    --output my-model

This should be relatively fast, but could take several hours for millions of rows.

In principle, existing tokenizers trained on natural language could work, but they have much larger vocabularies which are largely unused in SMILES.

Model pretraining

LChemME performs semi-supervised pretraining on a SMILES canonicalization task. This requires an understanding of chemical connectivity and atom precedence rules, forcing the model to build an internal representation of the chemical graph.

lchemme pretrain \
    --train hf://scbirlab/fang-2023-biogen-adme@scaffold-split:train \
    --column smiles \
    --test hf://scbirlab/fang-2023-biogen-adme@scaffold-split:test \
    --model facebook/bart-base \
    --tokenizer my-model \
    --epochs 0.5 \
    --output my-model \
    --plot my-model/training-log

If you want to continue training, you can do so with the --resume flag.

lchemme pretrain \
    --train hf://scbirlab/fang-2023-biogen-adme@scaffold-split:train \
    --column smiles \
    --test hf://scbirlab/fang-2023-biogen-adme@scaffold-split:test \
    --model my-model \
    --epochs 0.5 \
    --output my-model \
    --plot my-model/training-log \
    --resume

The dataset state can only be restored if the --model was trained with LChemME and the dataset configuration is identical, i.e. --train, --column are the same.

Featurizing

With a trained model, you can generate embeddings of your chemical datasets, optionally with UMAP plots colored by chemical properties.

lchemme featurize \
    --train hf://scbirlab/fang-2023-biogen-adme@scaffold-split:train \
    --column smiles \
    --model my-model \
    --batch-size 16 \
    --method mean \
    --plot umap \
> featurized.csv

You can specify one or several aggregation functions with --method. LChemME aggregates the sequence dimension of the encoder and decoder, then concatenates them.

If you want to use additional columns containing numerical values to color the UMAP plots, provide the column names under --extras.

Documentation

(Full API documentation to come at ReadTheDocs.)