We introduce VeGA-RX, conditioned on dual semantic descriptors (SMILES + SMARTS-RX), and VeGA-SCX, which integrates topological guidance (SMILES + Scaffold + SMARTS-RX). Both models efficiently explore chemical space but with distinct profiles: VeGA-SCX prioritizes structural discipline, while VeGA-RX maximizes generative freedom.

SMARTS-RX → SMILES Molecular Generator

This repository provides a complete pipeline for training, fine-tuning, and generating molecules using conditional SMARTS-RX → SMILES models.

The workflow is organized into six notebooks.

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1. Installation

First, download the codebase. Then, use conda to set up a new environment for VeGA. If you're new to conda, we recommend checking out this tutorial before proceeding.

conda env create -f enviroment.yml  
conda activate vega
python -m pip install tensorflow[and-cuda]  
conda install -c conda-forge jupyter notebook

Now, you can launch one of the three notebooks using Jupyter Notebook.

Workflow Overview

Recommended order of execution:

Training → (Optional) Fine-Tuning → Generation

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1. Training

1.1 def_train_SMART_SCAFFOLD.ipynb

Purpose

Train the SMARTS + Scaffold conditioned SMILES generator.

Setup

Modify the file paths in the configuration section:

• SMILES dataset
• SMARTS definition file
• Output/cache paths

Execution

Run the main training cell.

If train_data.pkl and val_data.pkl do NOT exist, the notebook starts full preprocessing and training: logger.info("🚀 START TRAINING: SMARTS-RX → SMILES")

If cached files already exist, it directly loads them:

logger.info("🚀 AVVIO SCRIPT: CARICAMENTO DIRETTO E ADDESTRAMENTO")

In most cases, simply running the main training cell is sufficient.

Output

• Trained model (.keras)
• char2idx.pkl
• idx2char.pkl
• vocab.json

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1.2 train_smartrx.ipynb

Purpose

Train a SMARTS-RX conditioned model (alternative implementation).

Setup

Update:

• Dataset path
• SMARTS file
• Output directory

Execution

Run all cells sequentially.

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2. Fine-Tuning

• GPU acceleration is recommended for training.

2.1 fine_tuning_SMART_SCAFFOLD.ipynb

Purpose

Fine-tune a pretrained SMARTS + Scaffold model.

Setup

Modify:

• PRETRAINED_MODEL
• VOCAB_PATH
• SMARTS file
• Fine-tuning dataset
• SAVE_DIR

Ensure MAX_LENGTH matches the original training configuration.

Execution

Run all cells.

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2.2 fine_tuning_smart.ipynb

Purpose

Fine-tune a SMARTS-only conditioned model.

Setup

Update:

• Pretrained model path
• Vocabulary files
• Dataset path
• Save directory

Execution

Run all cells sequentially.

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3. Generation

gen_def.ipynb

Purpose

Interactive molecule generation.

Supports:

• SMARTS conditioning
• Scaffold conditioning
• Combined SMARTS + Scaffold
• Unconditional generation
• Batch generation with CSV export

Setup

Update:

• Model path (.keras)
• char2idx.pkl
• idx2char.pkl
• vocab.json

Execution

Run the notebook.

An interactive menu will appear in the console.
Follow the prompts to:

• Select generation mode
• Set batch size
• Adjust temperature
• Provide SMARTS and/or scaffold inputs

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Notes

• Keep MAX_LENGTH consistent across all notebooks.
• Vocabulary files must correspond to the specific trained model.

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Publication

https://pubs.acs.org/doi/10.1021/acs.jcim.6c00535