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mist-models/
mist-28M-ti624ev1

Name: mist-28M-ti624ev1
Availability: InStock

molecular-property-prediction

2510.18900

apache-2.0

endpoints_compatible

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MIST: Molecular Insight SMILES Transformers

MIST is a family of molecular foundation models for molecular property prediction. The models were pre-trained on SMILES strings from the Enamine REAL Space dataset using the Masked Language Modeling (MLM) objective, then fine-tuned for downstream prediction tasks. Further information is available in our pre-print on arVix.

Model Details

Model Description

This is a pre-trained MIST encoder with 28M parameters trained on ~14B molecular SMILES tokens.

Developed by: Electrochemical Energy Group, University of Michigan, Ann Arbor.
Model type: Self-supervised pre-trained MIST encoder with supervised finetuning.

Model Sources

Repository: Full MIST Code
Paper: arVix Preprint
Demo: Finetuning and Inference Demo

Uses

How to Get Started with the Model

Use the code below to get started with the model.

Setting Up Your Environment

Create a virtual environment and install dependencies:

bash

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Note: SMIRK tokenizers require Rust to be installed. See the Rust installation guide for details.

or install uv and the run scripts below with uv run ....

Zero-Shot Similarity

Evaluating molecular similarity using MIST embeddings.

Demo Code

python

  """
  Minimal example for getting embeddings from a pretrained model and calculating similarity.

  Usage:
      uv run embeddings_similarity.py

  """
  # /// script
  # requires-python = ">=3.9"
  # dependencies = [
  #     "torch>=2.0.0",
  #     "transformers>=4.30.0",
  #     "smirk>=0.1.0",
  #     "numpy>=1.20.0",
  #     "rdkit>=2022.0.0",
  # ]
  # ///
  

  import torch
  import numpy as np
  from rdkit import Chem
  from smirk import SmirkTokenizerFast
  from transformers import AutoModel, AutoTokenizer


  def kekulize_smiles(smiles):
      """Convert SMILES to kekulized form."""
      mol = Chem.MolFromSmiles(smiles)
      if mol is None:
          raise ValueError(f"Invalid SMILES: {smiles}")
      Chem.Kekulize(mol)
      return Chem.MolToSmiles(mol, kekuleSmiles=True)


  def get_embeddings(smiles_list, model, tokenizer, device="cpu"):
      """Get embeddings for a list of SMILES strings."""
      # MIST was pretrained on Kekulize SMILES
      kekulized_smiles = [kekulize_smiles(s) for s in smiles_list]

      # Tokenize
      inputs = tokenizer(
          kekulized_smiles,
          padding=True,
          truncation=True,
          max_length=512,
          return_tensors="pt"
      )

      # Move to device
      inputs = {k: v.to(device) for k, v in inputs.items()}

      # Get embeddings
      model.eval()
      with torch.no_grad():
          outputs = model(**inputs)
          embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()

      return embeddings


  def cosine_similarity(emb1, emb2):
      """Calculate cosine similarity between two embeddings."""
      dot_product = np.dot(emb1, emb2)
      norm1 = np.linalg.norm(emb1)
      norm2 = np.linalg.norm(emb2)
      return dot_product / (norm1 * norm2)


  def main():
      # Load pretrained model
      model_path = "mist-models/mist-28M-ti624ev1"
      print(f"Loading model from {model_path}...")
      tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
      model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      model = model.to(device)
      print(f"Using device: {device}")

      # Example SMILES
      smiles_list = [
          "CCO",           # Ethanol
          "CC(C)O",        # Isopropanol
          "CCCO",          # Propanol
          "c1ccccc1",      # Benzene
          "c1ccccc1O",     # Phenol
          "CC(=O)O",       # Acetic acid
      ]

      print(f"\nGetting embeddings for {len(smiles_list)} molecules...")
      embeddings = get_embeddings(smiles_list, model, tokenizer, device)
      print(f"Embedding shape: {embeddings.shape}")

     
      # Query similarity example
      query = "CCCCO"  # Butanol
      print(f"Query: {query}")

      query_emb = get_embeddings([query], model, tokenizer, device)[0]

      print("\nMost similar molecules:")
      query_sims = []
      for i, smiles in enumerate(smiles_list):
          sim = cosine_similarity(query_emb, embeddings[i])
          query_sims.append((smiles, sim))

      query_sims.sort(key=lambda x: x[1], reverse=True)
      for i, (smiles, sim) in enumerate(query_sims[:3], 1):
          print(f"  {i}. {smiles:15s} : {sim:.4f}")

  if __name__ == "__main__":
        main()

Fine-tuning for Property Prediction

Fine-tune a pre-trained MIST encoder for a dataset/ property of your interest.

Demo Code

python

  """
  Minimal example for finetuning a pretrained model on a CSV using HuggingFace Trainer.

  CSV format:
      smiles,target
      CCO,1.23
      CC(C)O,2.45

  Usage:
      uv run finetune_minimal.py
  """

  # /// script
  # requires-python = ">=3.9"
  # dependencies = [
  #     "torch>=2.0.0",
  #     "transformers>=4.30.0",
  #     "datasets>=2.0.0",
  #     "smirk>=0.1.0",
  #     "accelerate>=0.26.0",
  #     "rdkit>=2022.0.0",
  # ]
  # ///

  import torch
  import torch.nn as nn
  from rdkit import Chem
  from smirk import SmirkTokenizerFast
  from datasets import load_dataset
  from transformers import (
      AutoModel,
      AutoTokenizer,
      Trainer,
      TrainingArguments,
      DataCollatorWithPadding,
  )
  from pathlib import Path


  def kekulize_smiles(smiles):
      """Convert SMILES to kekulized form."""
      mol = Chem.MolFromSmiles(smiles)
      if mol is None:
          raise ValueError(f"Invalid SMILES: {smiles}")
      Chem.Kekulize(mol)
      return Chem.MolToSmiles(mol, kekuleSmiles=True)


  class RegressionModel(nn.Module):
      """Model with encoder + regression task head."""

      def __init__(self, encoder, hidden_size=768, dropout=0.1):
          super().__init__()
          self.encoder = encoder
          self.task_head = nn.Sequential(
              nn.Linear(hidden_size, hidden_size),
              nn.ReLU(),
              nn.Dropout(dropout),
              nn.Linear(hidden_size, 1),
          )

      def forward(self, input_ids, attention_mask, labels=None):
          # Get encoder outputs
          encoder_output = self.encoder(
              input_ids=input_ids, attention_mask=attention_mask
          )
          # Use first token
          pooled = encoder_output.last_hidden_state[:, 0, :]

          # Regression prediction
          logits = self.task_head(pooled)

          loss = None
          if labels is not None:
              loss_fn = nn.MSELoss()
              loss = loss_fn(logits.squeeze(-1), labels)

          return {"loss": loss, "logits": logits} if loss is not None else {"logits": logits}


  def tokenize_function(examples, tokenizer):
      """Tokenize SMILES strings (kekulized)."""
      # MIST was pretrained on kekulized SMILES
      kekulized = [kekulize_smiles(s) for s in examples["smiles"]]
      return tokenizer(
          kekulized,
          padding="max_length",
          truncation=True,
          max_length=512,
      )


  def main():
      # 1. Load dataset from CSV
      dataset = load_dataset(
          "csv",
          data_files={"train": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm9.csv"},
      )["train"]

      # Split into train/test
      dataset = dataset.train_test_split(test_size=0.2, seed=42)

      # 2. Load pretrained encoder and tokenizer
      model_path = "mist-models/mist-28M-ti624ev1"
      tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
      encoder = AutoModel.from_pretrained(model_path, trust_remote_code=True)

      # 3. Create regression model with task head
      model = RegressionModel(
          encoder=encoder,
          hidden_size=encoder.config.hidden_size,
          dropout=0.1,
      )

      # 4. Tokenize dataset
      dataset = dataset.map(
          lambda x: tokenize_function(x, tokenizer),
          batched=True,
          desc="Tokenizing",
      )

      # Rename target column to labels (Trainer expects this)
      dataset = dataset.rename_column("lumo", "labels")

      # Set format for PyTorch
      dataset.set_format(
          type="torch",
          columns=["input_ids", "attention_mask", "labels"],
      )

      # 5. Setup training arguments
      training_args = TrainingArguments(
          output_dir="./finetuned_model",
          num_train_epochs=10,
          per_device_train_batch_size=32,
          per_device_eval_batch_size=32,
          learning_rate=1e-5,
          warmup_ratio=0.1,
          logging_steps=10,
          eval_strategy="epoch",
          save_strategy="epoch",
          load_best_model_at_end=True,
          metric_for_best_model="eval_loss",
          save_total_limit=2,
          report_to="none",  # Disable wandb/tensorboard
      )

      # 6. Create data collator
      data_collator = DataCollatorWithPadding(tokenizer)

      # 7. Create Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=dataset["train"],
          eval_dataset=dataset["test"],
          processing_class=tokenizer,
          data_collator=data_collator,
      )

      # 8. Train!
      print("Starting training...")
      trainer.train()

      # 9. Save final model
      print("Saving model...")
      trainer.save_model("./finetuned_model")
      tokenizer.save_pretrained("./finetuned_model")
      print("Done!")

      # 10. Inference example with the finetuned model
      test_smiles = [
          "CCO",
          "CC(C)O",
          "CCCC",
          "c1ccccc1",
          "CC(=O)O"
      ]

      # Kekulize and tokenize
      kekulized_test = [kekulize_smiles(s) for s in test_smiles]
      inputs = tokenizer(
          kekulized_test,
          padding=True,
          truncation=True,
          max_length=512,
          return_tensors="pt"
      )

      # Move to device and run inference
      device = next(model.parameters()).device
      inputs = {k: v.to(device) for k, v in inputs.items()}

      model.eval()
      with torch.no_grad():
          outputs = model(**inputs)
          predictions = outputs["logits"].squeeze(-1).cpu()

      print("\nPredictions:")
      for smiles, pred in zip(test_smiles, predictions):
          print(f"  {smiles} → {pred.item():.4f} Hartree")


    if __name__ == "__main__":
        main()

Use and Restrictions

Model weights are provided as-is for research purposes only, without guarantees of correctness, fitness for purpose, or warranties of any kind.

Research use only
No redistribution without permission
No commercial use without licensing agreement

Training Details

Training Data

We use the the Enamine REAL Space dataset to pretrain MIST models. At time of writing, Enamine REAL Space is the largest database of commercially available compounds. The dataset was constructed using forward synthetic analysis: experimentally validated building blocks were converted into synthons annotated with reactivity features. Enamine REAL Space was selected as the pretraining dataset since it was the largest database of molecular SMILES at the time of training, it is easily accessible for academic use and molecules relevant to downstream tasks, such as drug candidates, electrolytes, fragrances, live in synthetically accessible regions of chemical space.

Training Procedure

Inputs

The input to MIST models are SMILES strings for molecules. This model was pretrained and on Kekulized SMILES strings.

Model Architecture and Objective

Encoder: RoBERTa-PreLayerNorm encoder with 8 layers, a hidden size of 512, intermediate size of 2048, 8 attention heads and maximum sequence length of 2048.
Objective: MLM (Masked Language Modeling)
Loss: Cross-Entropy Loss
Optimizer: deepspeed.ops.lamb.FusedLAMB

Compute Infrastructure

Hardware

This model was pre-trained on 2 NVIDIA A100-SXM4-80GB in ~12 hours and 15 minutes.

Software

This model was trained with PyTorchLightning using the DeepSpeed strategy for data distributed parallelism. Model are exported in a Safetensors format.

Citation

If you use this model in your research, please cite:

bibtex

@online{MIST,
  title = {Foundation Models for Discovery and Exploration in Chemical Space},
  author = {Wadell, Alexius and Bhutani, Anoushka and Azumah, Victor and Ellis-Mohr, Austin R. and Kelly, Celia and Zhao, Hancheng and Nayak, Anuj K. and Hegazy, Kareem and Brace, Alexander and Lin, Hongyi and Emani, Murali and Vishwanath, Venkatram and Gering, Kevin and Alkan, Melisa and Gibbs, Tom and Wells, Jack and Varshney, Lav R. and Ramsundar, Bharath and Duraisamy, Karthik and Mahoney, Michael W. and Ramanathan, Arvind and Viswanathan, Venkatasubramanian},
  date = {2025-10-20},
  eprint = {2510.18900},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2510.18900},
  url = {http://arxiv.org/abs/2510.18900},  
}

Model Card Authors

Anoushka Bhutani, Alexius Wadell

Model Card Contact

For questions, issues, or licensing inquiries, please contact Venkat Viswanathan venkvis@umich.edu.