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ChemEmbed-v01

Sentence Similarity
sentence-transformers
Safetensors
code
bert
feature-extraction
Generated from Trainer
dataset_size:1,183,174
loss:CosineSimilarityLoss
chemistry
Eval Results
text-embeddings-inference
Inference Endpoints
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metadata
datasets:
  - COCONUTDB
language:
  - code
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1,183,174
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: >-
      [O][=C][Branch2][Branch2][Ring1][O][C][C][Branch2][Ring1][=Branch1][O][C][=Branch1][C][=O][C][C][C][C][C][C][=C][C][C][C][C][C][C][C][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C][Branch2][Ring1][Branch1][O][C][O][C][Branch1][Ring1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][#Branch2][O][C][Branch1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring2][Ring1][Branch1][O][C][O][C][Branch1][Ring1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][#Branch2][O][C][C][C][C][C][C][C][C][C][C][C][C][C][C]
    sentences:
      - >-
        [O][=C][Branch2][Ring1][N][N][N][=C][Branch1][N][C][=C][C][=C][Branch1][C][Cl][C][=C][Ring1][#Branch1][C][=C][C][=C][Branch1][C][Cl][C][=C][Ring1][#Branch1][C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][Ring1][#Branch2][O]
      - >-
        [O][=C][Branch1][C][O][C][=C][Branch1][C][C][C][C][=Branch1][C][=O][O][C]
      - >-
        [O][=C][Branch1][C][O][C][C][C][C][C][C][C][C][Branch1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][#C][C][Branch1][C][O][C][C][C][C]
  - source_sentence: '[O][=C][Branch1][#Branch1][O][C][Branch1][C][C][C][C][C][C][C][C][C][C][C]'
    sentences:
      - >-
        [O][=C][O][C][C][Branch1][C][O][C][C][Ring1][#Branch1][=C][C][C][C][C][C][C][C][C][C][C][C][C][C]
      - >-
        [O][=C][C][=C][C][O][C][O][C][=Ring1][Branch1][C][=C][Ring1][=Branch2][Br]
      - >-
        [O][=C][Branch2][#Branch1][=C][O][C][C][=Branch1][C][=C][C][C][Branch1][#Branch1][O][C][=Branch1][C][=O][C][C][C][C][=Branch1][C][=O][C][Branch2][=Branch1][Ring1][O][C][C][Ring1][Branch2][Branch1][C][C][C][Ring1][=Branch1][Branch1][C][O][C][Branch1][#Branch1][O][C][=Branch1][C][=O][C][C][Branch1][#Branch1][O][C][=Branch1][C][=O][C][C][Ring2][Ring1][N][Branch1][#C][C][O][C][=Branch1][C][=O][C][=C][C][=C][C][=C][Ring1][=Branch1][C][Branch1][Branch2][C][O][C][=Branch1][C][=O][C][C][Ring2][Ring2][S][C][C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]
  - source_sentence: >-
      [O][=C][O][C][=C][Branch2][Ring1][#C][C][=C][C][O][C][N][Branch1][S][C][=C][C][=C][Branch1][=Branch1][O][C][C][C][C][C][=C][Ring1][O][C][C][Ring2][Ring1][Branch1][=Ring1][P][C][=Branch1][=C][=C][Ring2][Ring1][=Branch2][C][C][=C][C][=C][C][=C][Ring1][=Branch1][C]
    sentences:
      - >-
        [O][=C][N][C][Branch1][S][C][=Branch1][C][=O][N][C][=C][C][=C][C][=C][Ring1][N][Ring1][=Branch1][C][C][=Branch1][C][=O][N][C][C][C][=N][C][=Branch1][Branch1][=C][S][Ring1][Branch1][C]
      - >-
        [O][=C][C][=C][Branch2][Branch2][O][O][C][=C][C][Branch2][Ring2][#C][O][C][C][Branch1][Ring1][C][O][C][C][C][=C][C][NH1][C][=C][C][=Ring1][Branch1][C][=C][Ring1][=Branch2][C][C][=Branch1][C][=O][C][Branch1][Ring1][C][O][C][C][=Branch1][Branch2][=C][Ring2][Ring1][#C][Ring2][Ring1][O][C][Ring1][#Branch2][=C][C][Branch2][Ring2][O][O][C][Branch1][Ring2][C][Ring1][Branch1][C][Branch1][C][O][C][Branch2][Ring1][Branch2][C][=C][C][C][Branch1][S][N][Branch1][=Branch1][C][C][C][O][C][C][C][C][Ring1][S][Ring1][O][C][C][C][C][C][O][=C][Ring2][Branch1][=Branch2][C][O][C][=Branch1][C][=O][O][C][C]
      - >-
        [O][C][C][O][C][Branch2][Branch2][#Branch2][O][C][C][Branch1][C][O][C][Branch1][C][O][C][Branch2][#Branch1][#Branch2][O][C][Ring1][Branch2][O][C][C][C][C][Branch2][=Branch1][=N][C][=Branch2][Branch1][P][=C][C][Branch1][C][O][C][Branch1][C][C][C][Ring1][Branch2][C][C][Branch1][C][O][C][C][Branch1][=Branch2][C][C][C][Ring1][O][Ring1][Branch1][C][C][Branch2][Ring1][Branch1][O][C][O][C][Branch1][Ring1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][#Branch2][O][Branch1][C][C][C][C][=C][C][Branch1][C][C][C][C][Ring2][Ring2][=Branch2][Branch1][C][C][C][C][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring2][Branch1][S][O]
  - source_sentence: >-
      [O][=C][O][C][=C][C][=C][Branch1][O][O][C][=Branch1][C][=O][N][Branch1][C][C][C][C][=C][Ring1][N][C][=Branch1][Ring2][=C][Ring1][S][C][O][C][=C][C][=C][C][=C][Ring1][=Branch1][C][=Ring1][=Branch2]
    sentences:
      - >-
        [O][=C][C][=Branch2][#Branch1][=N][=C][C][Branch2][Ring2][N][C][NH1+1][C][=Branch2][Ring2][Ring1][=C][Branch1][#Branch2][C][=N][C][=C][C][Ring1][Branch2][Ring1][Branch1][C][C][=C][C][=Branch1][S][=C][C][=Branch1][Ring2][=C][Ring1][=Branch1][C][C][C][O][C][C][Ring1][=Branch1][C][C][C][O][C][Branch1][C][O][C][C][Branch1][C][C][C][C][C][=Branch1][C][=O][C][Branch1][C][C][Branch1][C][C][C][Ring1][#Branch2][C][C][C][Ring1][=C][Branch1][C][C][C][Ring2][Ring2][=C][Branch1][C][C][C][Ring2][Branch1][C][C][Branch1][C][C][C][C][Branch1][C][O][C][O][C][Ring1][Ring1][Branch1][C][C][C]
      - >-
        [O][=C][Branch1][C][O][C][Branch2][O][P][O][C][C][=Branch1][C][=O][N][C][C][Branch1][C][O][C][C][Branch2][=Branch2][=Branch2][O][C][C][=Branch1][C][=O][N][C][C][Branch1][C][O][C][C][Branch2][=Branch1][P][O][C][C][Branch1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Branch2][Branch1][#Branch2][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C][Branch2][Ring1][O][N][C][=Branch1][C][=O][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][Branch1][C][O][C][=C][C][C][C][C][C][C][C][C][C][C][C][C][Ring2][Branch1][#Branch1][O][Branch1][P][O][C][Ring2][Branch1][S][C][Branch1][C][O][C][Branch1][C][O][C][O][C][C][=Branch1][C][=O][O][Branch1][P][O][C][Ring2][#Branch1][=Branch1][C][Branch1][C][O][C][Branch1][C][O][C][O][C][C][=Branch1][C][=O][O][O][C][Branch1][N][C][Branch1][C][O][C][Branch1][C][O][C][O][C][C][Branch1][Branch2][N][C][=Branch1][C][=O][C][O][C][Branch1][C][O][C][Ring2][=Branch2][Branch2]
      - >-
        [O][=C][Branch1][C][N][C][=N][C][=C][C][=C][C][=C][Ring1][=Branch1][C][=Branch1][C][=O][N][Ring1][O][C][C][O][C]
  - source_sentence: '[O][=C][Branch1][#Branch1][C][=C][C][C][C][=C][C]'
    sentences:
      - >-
        [O][=C][Branch1][C][O][C][C][C][C][C][C][C][C][Branch1][C][O][C][=C][C][#C][C][=C][C][C][C]
      - >-
        [O][C][C][O][C][Branch2][=Branch2][Ring1][O][C][Branch1][C][C][C][C][C][Branch1][C][O][O][C][C][C][C][C][C][C][C][C][Branch2][Branch1][N][O][C][O][C][Branch1][Ring1][C][O][C][Branch2][Ring2][#Branch1][O][C][O][C][Branch1][Ring1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][#Branch2][O][C][O][C][Branch1][C][C][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][=Branch2][O][C][Branch1][C][O][C][Ring2][Ring1][#C][O][C][C][C][Ring2][Ring2][#Branch1][Branch1][C][C][C][Ring2][Ring2][N][C][C][C][Ring2][Ring2][S][Branch1][C][C][C][Ring2][Branch1][Ring2][C][Ring2][Branch1][Branch2][C][C][Branch1][C][O][C][Branch1][C][O][C][Ring2][=Branch1][=Branch1][O]
      - >-
        [O][=C][Branch1][#Branch2][C][=C][C][#C][C][#C][C][#C][C][N][C][C][C][=C][C][=C][C][=C][Ring1][=Branch1]
model-index:
  - name: SentenceTransformer
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: NP isotest
          type: NP-isotest
        metrics:
          - type: pearson_cosine
            value: 0.936731178796972
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.93027366634068
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.826340669261792
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.845192256146849
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.842726066770598
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.865381289346298
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.924283770507162
            name: Pearson Dot
          - type: spearman_dot
            value: 0.923230424410894
            name: Spearman Dot
          - type: pearson_max
            value: 0.936731178796972
            name: Pearson Max
          - type: spearman_max
            value: 0.93027366634068
            name: Spearman Max

ChEmbed v0.1 - Chemical Embeddings

This prototype is a sentence-transformers based on MiniLM-L6-H384-uncased fine-tuned on around 1 million pairs of valid natural compounds' SELFIES (Krenn et al. 2020) taken from COCONUTDB (Sorokina et al. 2021). It maps compounds' Self-Referencing Embedded Strings (SELFIES) into a 768-dimensional dense vector space, potentially can be used for chemical similarity, similarity search, classification, clustering, and more.

I am planning to train this model with more epochs on current dataset, before moving on to a larger dataset with 6 million pairs generated from ChemBL34. However, this will take some time due to computational and financial constraints. A future project of mine is to develop a custom model specifically for cheminformatics to address any biases and optimization issues in repurposing an embedding model designed for NLP tasks.

Disclaimer: For Academic Purposes Only

The information and model provided is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes. The author do not guarantee the accuracy, completeness, or reliability of the information.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: MiniLM-L6-H384-uncased
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset: SELFIES pairs generated from COCONUTDB
  • Language: SELFIES
  • License: CC BY-NC 4.0

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': True, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': False})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("gbyuvd/ChEmbed-v01")
# Run inference
sentences = [
    '[O][=C][Branch1][#Branch1][C][=C][C][C][C][=C][C]',
    '[O][=C][Branch1][C][O][C][C][C][C][C][C][C][C][Branch1][C][O][C][=C][C][#C][C][=C][C][C][C]',
    '[O][C][C][O][C][Branch2][=Branch2][Ring1][O][C][Branch1][C][C][C][C][C][Branch1][C][O][O][C][C][C][C][C][C][C][C][C][Branch2][Branch1][N][O][C][O][C][Branch1][Ring1][C][O][C][Branch2][Ring2][#Branch1][O][C][O][C][Branch1][Ring1][C][O][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][#Branch2][O][C][O][C][Branch1][C][C][C][Branch1][C][O][C][Branch1][C][O][C][Ring1][=Branch2][O][C][Branch1][C][O][C][Ring2][Ring1][#C][O][C][C][C][Ring2][Ring2][#Branch1][Branch1][C][C][C][Ring2][Ring2][N][C][C][C][Ring2][Ring2][S][Branch1][C][C][C][Ring2][Branch1][Ring2][C][Ring2][Branch1][Branch2][C][C][Branch1][C][O][C][Branch1][C][O][C][Ring2][=Branch1][=Branch1][O]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Dataset

Dataset Reference Number of Pairs
COCONUTDB (0.8:0.1:0.1 split) (Sorokina et al. 2021) 1,183,174

Evaluation

Metrics

Semantic Similarity

Metric Value
pearson_cosine 0.9367
spearman_cosine 0.9303
pearson_manhattan 0.8263
spearman_manhattan 0.8452
pearson_euclidean 0.8654
spearman_euclidean 0.9243
pearson_dot 0.9232
spearman_dot 0.9367
pearson_max 0.9303
spearman_max 0.8961

Limitations

For now, the model might be ineffective in embedding synthetic drugs, since it is still trained on just natural products. Also, the tokenizer used is still uncustomized one.

Testing Generated Embeddings' Clusters

The plot below shows how the model's embeddings (at this stage) cluster different classes of compounds, compared to using MACCS fingerprints.

image/png

image/png

Framework Versions

  • Python: 3.9.13
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.3.1+cu121
  • Accelerate: 0.31.0
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

Contact

G Bayu ([email protected])