🧬 Multi-Spectral Molecular Structure Elucidation

Deep Learning Framework for Molecular Structure Elucidation from Multi-Modal Spectroscopic Data

🚀 Overview

This repository presents a comprehensive deep learning framework for automated molecular structure elucidation from multi-modal spectroscopic data. Our approach combines transformer-based architectures with advanced molecular representation learning to predict SMILES molecular structures from diverse analytical chemistry inputs.

🎯 Key Capabilities

• Multi-Modal Spectroscopy Integration: Seamlessly processes IR, UV-Vis, NMR (¹H, ¹³C, ¹⁹F, ¹⁵N, ¹⁷O), and high-resolution mass spectrometry data
• Transformer-Based Architecture: State-of-the-art attention mechanisms for complex spectral-structural relationships
• Molecular Generation: Autoregressive SMILES generation with constraint-guided beam search
• Self-Supervised Learning: Large-scale molecular pre-training with masked language modeling
• Multi-Task Learning: Joint optimization of structure prediction and chemical property estimation

🏗️ Architecture

graph TB
    A[Multi-Modal Spectral Input] --> B[SpectroMol Module]
    C[Molecular SMILES Data] --> D[MS Mol2Mol Module]
    
    B --> E[Custom Transformer Encoder]
    D --> F[Molecular Transformer]
    
    E --> G[SMILES Decoder]
    F --> H[Pre-trained Representations]
    
    G --> I[Molecular Structure Output]
    H --> I
    
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style I fill:#e8f5e8

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📁 Project Structure

🔬 SpectroMol - Multi-Modal Spectral Analysis

Advanced deep learning framework for molecular structure prediction from diverse spectroscopic data.

Core Components:

• Multi-Modal Encoder: Custom transformer architecture for spectral feature processing
• SMILES Decoder: Autoregressive molecular structure generation
• Auxiliary Tasks: Joint learning of chemical properties and molecular descriptors
• Attention Analysis: Interpretable spectral-structural relationships

Key Features:

• 8 spectroscopic modalities (IR, UV, ¹H/¹³C NMR, HSQC, COSY, J-coupling, MS)
• Temperature-controlled sampling and beam search
• Comprehensive evaluation metrics (BLEU, fingerprint similarity, validity)
• Robustness analysis and t-SNE visualization

🧪 MS Mol2Mol - Molecular Representation Learning

Self-supervised molecular transformer for large-scale chemical understanding and generation.

Core Components:

• Pre-training Framework: Masked language modeling on molecular sequences
• Fine-tuning System: Task-specific adaptation with SMILES corruption
• Inference Engine: Constraint-guided molecular generation
• Mass Spectrometry Integration: Enhanced molecular understanding with MS data

Key Features:

• Transformer encoder-decoder with atomic feature integration
• Advanced tokenization for complex molecular structures
• Distributed training with gradient accumulation
• Beam search and temperature sampling strategies

📃 Datasets

This project utilizes comprehensive datasets hosted on Hugging Face, providing both molecular pre-training data and multi-modal spectroscopic datasets for structure elucidation tasks.

🗃️ Dataset Collection

Main Dataset Hub: multi-spec-elucidation

Our dataset collection includes two primary components:

🧪 MS Mol2Mol Pre-training Dataset

• Repository: ms-mol2mol
• Content: Large-scale molecular dataset containing 400M molecules for self-supervised pre-training
• Purpose: Molecular representation learning and transformer pre-training
• Format: SMILES sequences with molecular properties and descriptors
• Applications: Foundation model development, molecular understanding, and chemical space exploration

🔬 SpectroMol QM9 Spectral Dataset

• Repository: qm9_all_raw_spe
• Content: Comprehensive multi-modal spectroscopic data derived from QM9 molecules
• Modalities: IR, UV-Vis, NMR (¹H, ¹³C, ¹⁹F, ¹⁵N, ¹⁷O), and mass spectrometry data
• Purpose: Training and evaluation of multi-modal structure elucidation models
• Format: Processed spectral features paired with ground-truth SMILES structures
• Applications: Multi-modal learning, spectral-structural relationship modeling, and analytical chemistry AI

📥 Data Access

# Download datasets using Hugging Face datasets library
from datasets import load_dataset

# Load MS Mol2Mol pre-training data
ms_mol2mol_dataset = load_dataset("zhiyuanyan1/multi-spec-elucidation", data_dir="ms-mol2mol")

# Load SpectroMol QM9 spectral data
spectromol_dataset = load_dataset("zhiyuanyan1/multi-spec-elucidation", data_dir="qm9_all_raw_spe")

🛠️ Installation

Prerequisites

# Core dependencies
Python >= 3.8
PyTorch >= 1.9.0
RDKit >= 2021.03
NumPy >= 1.21.0
Pandas >= 1.3.0

Environment Setup

# Clone repository
git clone https://github.com/your-repo/multi-spec-elucidation.git
cd multi-spec-elucidation

# Create conda environment
conda create -n mol-elucidation python=3.8
conda activate mol-elucidation

# Install PyTorch (adjust CUDA version as needed)
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

# Install RDKit
conda install -c rdkit rdkit

# Install additional dependencies
pip install -r requirements.txt

🚀 Quick Start

High-level Usage of SpectroMol: Multi-Modal Structure Prediction

from spectromol import AtomPredictionModel, predict_greedy

# Initialize model
model = AtomPredictionModel(
    vocab_size=vocab_size,
    count_tasks_classes=count_tasks,
    binary_tasks=binary_tasks
)

# Load pre-trained weights
model.load_state_dict(torch.load('spectromol/weights/best_model.pth'))

# Predict molecular structures from spectra
predicted_smiles = predict_greedy(
    model, ir_data, uv_data, cnmr_data, hnmr_data, mass_data,
    char2idx, idx2char, max_seq_length=100
)

High-level Usage of MS-Mol2Mol: Pre-training and Fine-tuning

from ms_mol2mol import MoleculePretrainingModel, train_model

# Pre-training on large molecular datasets
pretrain_model = MoleculePretrainingModel(
    vocab_size=vocab_size,
    atom_type_dim=6,
    d_model=512,
    nhead=8
)

# Self-supervised pre-training
train_model(pretrain_model, pretrain_dataloader, epochs=100)

# Fine-tuning for specific tasks
finetune_model(pretrain_model, task_dataloader, epochs=50)

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🧬 Advancing Molecular Discovery Through AI 🤖

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