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roberta_zinc_decoder

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Roberta Zinc Decoder

This model is a GPT2 decoder model designed to reconstruct SMILES strings from embeddings created by the roberta_zinc_480m model. The decoder model was trained on 30m compounds from the ZINC Database.

The decoder model conditions generation on mean pooled embeddings from the encoder model. Mean pooled embeddings are used to allow for integration with vector databases, which require fixed length embeddings.

Condition embeddings are passed to the decoder model using the encoder_hidden_states attribute. The standard GPT2LMHeadModel does not support generation with encoder hidden states, so this repo includes a custom ConditionalGPT2LMHeadModel. See example below for how to instantiate the model.

import torch
from transformers import AutoModelForCausalLM, RobertaTokenizerFast, RobertaForMaskedLM, DataCollatorWithPadding

tokenizer = RobertaTokenizerFast.from_pretrained("entropy/roberta_zinc_480m", max_len=256)
collator = DataCollatorWithPadding(tokenizer, padding=True, return_tensors='pt')

encoder_model = RobertaForMaskedLM.from_pretrained('entropy/roberta_zinc_480m')
encoder_model.eval();

commit_hash = '0ba58478f467056fe33003d7d91644ecede695a7'
decoder_model = AutoModelForCausalLM.from_pretrained("entropy/roberta_zinc_decoder",
                                                     trust_remote_code=True, revision=commit_hash)
decoder_model.eval();


smiles = ['Brc1cc2c(NCc3ccccc3)ncnc2s1',
 'Brc1cc2c(NCc3ccccn3)ncnc2s1',
 'Brc1cc2c(NCc3cccs3)ncnc2s1',
 'Brc1cc2c(NCc3ccncc3)ncnc2s1',
 'Brc1cc2c(Nc3ccccc3)ncnc2s1']

inputs = collator(tokenizer(smiles))
outputs = encoder_model(**inputs, output_hidden_states=True)
full_embeddings = outputs[1][-1]
mask = inputs['attention_mask']
mean_embeddings = ((full_embeddings * mask.unsqueeze(-1)).sum(1) / mask.sum(-1).unsqueeze(-1))

decoder_inputs = torch.tensor([[tokenizer.bos_token_id] for i in range(len(smiles))])

hidden_states = mean_embeddings[:,None] # hidden states shape (bs, 1, -1)

gen = decoder_model.generate(
              decoder_inputs,
              encoder_hidden_states=hidden_states,
              do_sample=False, # greedy decoding is recommended
              max_length=100, 
              temperature=1.,
              early_stopping=True,
              pad_token_id=tokenizer.pad_token_id,
                         )

reconstructed_smiles = tokenizer.batch_decode(gen, skip_special_tokens=True)

Model Performance

The decoder model was evaluated on a test set of 1m compounds from ZINC. Compounds were encoded with the roberta_zinc_480m model and reconstructed with the decoder model.

The following metrics are computed:

  • exact_match - percent of inputs exactly reconstructed
  • token_accuracy - percent of output tokens exactly matching input tokens (excluding padding)
  • valid_structure - percent of generated outputs that resolved to a valid SMILES string
  • tanimoto - tanimoto similarity between inputs and generated outputs. Excludes invalid structures
  • cos_sim - cosine similarity between input encoder embeddings and output encoder embeddings

eval_type=full reports metrics for the full 1m compound test set.

eval_type=failed subsets metrics for generated outputs that failed to exactly replicate the inputs.

eval_type exact_match token_accuracy valid_structure tanimoto cos_sim
full 0.948277 0.990704 0.994278 0.987698 0.998224
failed 0.000000 0.820293 0.889372 0.734097 0.965668

license: mit