LLM-based Chemical Retrosynthesis基于大语言模型的化学逆合成

Overview

This project applies Large Language Models to the critical challenge of chemical retrosynthesis prediction—determining the reactants needed to synthesize a target molecule. This work sits at the intersection of AI and chemistry, representing a significant application of AI for Science (AI4S).

Course: DS-GA-1011 NLP with Representation Learning (Grad Level)(Fall 2024)

Institution: New York University Center of Data Science

Advisor: Prof. He He

Team Members: Hongjia Huang, Zhaodong Liu, Haoqi (Kevin) Zhang

Repository: GitHub - NLP_project

Dataset: HuggingFace - USPTO-50K

Report: Final Report (PDF)

Problem Statement

Chemical retrosynthesis is crucial for drug discovery but traditionally requires extensive laboratory experimentation. Motivated by how large language models (LLMs) learn and manipulate latent structures, this project addresses a critical challenge in drug discovery: predicting chemical retrosynthesis from SMILES notation.

The core research question is: how can we effectively learn robust representations of complex molecular transformations, given that SMILES notation lacks atomic positional encoding?

The challenge is compounded by:

• Data scarcity: Raw chemical reaction data is expensive to obtain, error-prone, and insufficient in scale for robust training
• Representation limitations: SMILES notation is a discrete representation without explicit 3D structural information
• Data quality issues: Limited training data compared to typical NLP tasks, with high rates of incorrect or incomplete entries
• Reaction-type imbalance: Performance degradation for specific reaction types due to insufficient examples

Innovation: LLM-based Data Augmentation

Research Insight and Approach

After initial experiments using a pre-trained BART-based model on the USPTO-50K dataset, I conducted thorough error analysis and examined the incorrect prediction patterns across different reaction types. Data scarcity emerged as the key cause of performance degradation, particularly for specific reaction types with limited training examples.

Rather than accepting these constraints, I proposed an innovative approach: what if we could use a fine-tuned LLM to reconstruct and augment the dataset itself, effectively distilling better representations from limited lab data?

The key insight was that LLM-generated data could provide more learnable and generalizable molecular patterns, creating a higher-quality representation space for the smaller model to learn from. By filtering out incorrect entries and inserting valid, LLM-generated reactions, we created a corpus that dramatically improves the model’s ability to learn robust molecular transformation patterns.

Data Engineering Pipeline

I designed the experimental pipeline and oversaw both dataset engineering and model development:

1. Error Analysis: Systematically examined incorrect predictions to identify patterns of failure across different reaction types
2. Filtering: Identified and removed incorrect or incomplete reaction data from the original USPTO-50K dataset based on error analysis findings
3. LLM-based Generation: Used fine-tuned GPT-3.5-turbo to generate valid, chemically sound examples, focusing on underrepresented reaction types
4. Validation: Ensured generated data followed SMILES notation rules and chemical principles
5. Integration: Created USPTO-50K_γ with carefully balanced original and generated data

This demonstrated that representation learning has significant impact on learning stability, efficiency, and generalization in scientific domains.

The augmented dataset is publicly available on HuggingFace for reproducibility and community use.

Model Development

BART-based Architecture

We utilized BART (Bidirectional and Auto-Regressive Transformers) as our base model, which is particularly well-suited for sequence-to-sequence tasks like chemical retrosynthesis. BART combines the strengths of BERT’s bidirectional encoder and GPT’s autoregressive decoder.

Parameter-Efficient Fine-tuning (PEFT) Strategies

To make the approach practical for deployment, I applied parameter-efficient fine-tuning (PEFT) strategies that dramatically reduce computational requirements while maintaining performance. I implemented and compared multiple approaches:

1. Encoder Freezing: Selective layer freezing to preserve pre-trained knowledge while only training upper layers, reducing trainable parameters while maintaining domain-specific adaptation
2. MLP Adapters: Adding small adapter layers (bottleneck architecture) for task-specific learning without modifying the base model
3. LoRA (Low-Rank Adaptation): Efficient parameter updates using low-rank matrix decomposition, achieving full model performance with minimal trainable parameters

Key Achievement: By applying these PEFT strategies, I reduced trainable parameters by over 90% and cut training time significantly while retaining over 98% of full fine-tuning accuracy. This experience demonstrated that proper representation learning enables both high performance and computational efficiency.

Each approach was evaluated on both the original USPTO-50K dataset and our augmented USPTO-50K_γ dataset to measure the impact of data quality on model performance.

Results

Key Findings

The LLM-based data augmentation approach achieved remarkable improvements across all metrics:

Data Augmentation Impact:

• Top-1 Accuracy: 58.8% (baseline) → 74.2% (with USPTO-50K_γ) — 26.2% improvement
• Top-3 Accuracy: 73.9% (baseline) → 85.2% (with USPTO-50K_γ)
• Top-5 Accuracy: 79.4% (baseline) → 88.6% (with USPTO-50K_γ)

This 26.2% improvement in Top-1 Accuracy indicated that LLM-generated data provided more learnable and generalizable molecular patterns, creating a higher-quality representation space for the model to learn from. The augmented dataset enabled the model to better understand molecular transformation patterns, especially for underrepresented reaction types.

Parameter-Efficient Fine-tuning Results:

• LoRA Performance: Achieved 98% of full fine-tuning accuracy with only 0.17% trainable parameters
• MLP Adapters: Competitive performance with minimal architectural changes
• Frozen Layers: Effective for domain adaptation while preserving pre-trained knowledge

Computational Efficiency:

• Over 90% reduction in trainable parameters using LoRA
• Significant reduction in GPU memory requirements
• Faster training convergence with augmented data
• Lower computational costs while maintaining high accuracy

The combination of data augmentation and parameter-efficient fine-tuning demonstrates a cost-effective approach to adapting LLMs for specialized scientific tasks.

Impact & Contributions

This research demonstrates several important contributions to AI for Science:

1. Novel Perspective on Representation Learning: Exposed the limitations of existing discrete representations (SMILES notation) and demonstrated how proper data representation quality directly impacts model learning stability, efficiency, and generalization

2. LLM-based Representation Distillation: Proposed and validated an innovative approach where LLMs distill better representations from limited lab data, creating a higher-quality representation space (26.2% accuracy improvement) that enables smaller models to learn more effectively

3. Systematic Error Analysis Framework: Designed a comprehensive experimental pipeline involving error analysis across reaction types, identifying data scarcity as the root cause and developing targeted solutions for underrepresented reaction types

4. Parameter-Efficient Fine-tuning: Proved that PEFT strategies (encoder freezing, MLP adapters, LoRA) can reduce trainable parameters by over 90% while retaining over 98% of full fine-tuning accuracy, demonstrating that representation learning enables both high performance and computational efficiency

5. Open Dataset Release: Created and released USPTO-50K_γ on HuggingFace for community use and reproducibility, contributing to the broader AI4S community

6. Scalable Methodology: Demonstrated a methodology applicable to other chemistry tasks and scientific domains with limited training data, showing how representation learning can overcome data constraints in specialized domains

7. Drug Discovery Applications: Provided a practical tool for accelerating retrosynthesis planning in pharmaceutical research

Technical Skills & Tools

Machine Learning & NLP:

• BART (Bidirectional and Auto-Regressive Transformers) architecture
• Parameter-efficient fine-tuning (LoRA, adapters, frozen layers)
• Sequence-to-sequence modeling
• Transfer learning and domain adaptation
• LLM-based data augmentation (GPT-3.5-turbo)

Chemical Informatics:

• SMILES (Simplified Molecular Input Line Entry System) notation
• Chemical reaction representation
• Retrosynthesis prediction
• Molecular structure validation

Development & Tools:

• PyTorch deep learning framework
• Hugging Face Transformers library
• Python scientific computing (NumPy, Pandas)
• Dataset creation and curation
• Model evaluation metrics (Top-K accuracy)
• Experiment tracking and benchmarking

Future Directions

• Extending to multi-step retrosynthesis
• Incorporating 3D molecular structure information
• Exploring other chemistry-related prediction tasks