RetroGPT

RetroGPT

RetroGPT is a transformer-based model for single-step retrosynthetic prediction. Given a target product molecule in SMILES format, the model predicts the required reactant molecules through sequence-to-sequence generation.

Model Details

Model Description

RetroGPT is a lightweight transformer architecture specifically designed for chemical reaction prediction. The model learns to "reverse" chemical reactions by predicting precursor reactants from product molecules.

• Developed by: kssrikar4
• Model type: Transformer-based Sequence-to-Sequence
• Language: SMILES (Simplified Molecular Input Line Entry System)
• License: MIT

Model Sources

• Architecture: Custom transformer with RMSNorm, SwiGLU activation, and multi-head attention
• Code: Training code available upon request

Uses

Direct Use

The model is intended for retrosynthetic analysis in drug discovery and organic chemistry:

• Predicting reactant molecules for a given target product
• Assisting chemists in planning synthetic routes
• Educational purposes for teaching retrosynthetic thinking
• High-throughput virtual synthesis planning

Out-of-Scope Use

• Multi-step synthesis planning (requires integration with search algorithms)
• Predicting reaction conditions or yields
• Stereochemistry-specific predictions without additional fine-tuning

How to Get Started with the Model

import torch, sys
from transformers import AutoModelForCausalLM
from rdkit.Chem import AllChem, Draw
from IPython.display import display

def get_reaction(product, model_id="kssrikar4/RetroGPT"):
    model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()
    tok = getattr(sys.modules[model.__class__.__module__], "RetroGPTTokenizer").from_pretrained(model_id)
    ids = torch.tensor([tok.convert_tokens_to_ids(tok.tokenize(f"<s>{product}<sep>"))])
    
    out = model.generate(
        input_ids=ids, 
        attention_mask=torch.ones_like(ids),
        max_length=256, 
        num_beams=5, 
        num_return_sequences=1
    )

    reac = tok.decode(out[0].tolist(), skip_special_tokens=True).split("<sep>")[-1].replace(" ", "")
    rxn = AllChem.ReactionFromSmarts(f"{reac}>>{product}", useSmiles=True)
    
    if rxn:
        AllChem.Compute2DCoordsForReaction(rxn)
        display(Draw.ReactionToImage(rxn, subImgSize=(350, 350)))

get_reaction("your smiles")

Training

Training Data

The model was trained on the USPTO dataset (uspto.csv), which contains patent-derived chemical reactions extracted from US patents. The dataset includes:

• Single-product, multi-reactant reactions
• Canonicalized SMILES representations
• Reactions spanning multiple decades (1976-present)

Dataset Statistics:

• Training split: ~99% of data
• Validation split: ~1% of data
• Maximum sequence length: 256 tokens

Training Procedure

Architecture:

• Transformer layers: 6
• Hidden dimension (d_model): 512
• Attention heads: 8
• Vocabulary size: ~150 chemical tokens
• Positional encoding: Learned embeddings

Hyperparameters:

• Batch size: 64 (effective batch: 128 with gradient accumulation)
• Learning rate: 3e-4 with AdamW optimizer
• Weight decay: 0.01
• Dropout: 0.1
• Training epochs: 80
• Gradient accumulation steps: 2
• Learning rate scheduler: Cosine annealing

Optimization:

• Mixed precision training (AMP)
• Gradient clipping
• Distributed training support (DDP)

Training Hyperparameters

Evaluation

Evaluation Metrics

The model is evaluated using Top-k Accuracy based on exact canonical SMILES matching:

• Top-1 Accuracy: The exact correct reactant mixture is the model's first prediction
• Top-3 Accuracy: The correct answer appears in the top 3 beam search candidates
• Top-5 Accuracy: The correct answer appears in the top 5 candidates

Test Results

Performance on Validation Set:

• Top-1 Accuracy: 59.2%
• Top-3 Accuracy: 73.0%
• Top-5 Accuracy: 76.4%

Temporal Performance

The model maintains consistent performance across different patent years, demonstrating robust generalization to reactions from different time periods.

Token-Level Analysis

The confusion matrix reveals:

• Strong diagonal dominance: The model correctly predicts chemical tokens with high accuracy
• Aromatic vs. Aliphatic: Minor confusion between aromatic (c) and aliphatic (C) carbons
• Ring closures: Some challenges with numeric ring closure markers (1, 2, etc.)
• Branching syntax: Occasional misplacement of parentheses for molecular branches

Qualitative Examples

Disclaimer: This model is intended for research and educational purposes. Always verify predictions with chemical expertise and experimental validation before laboratory use.