Can Large Language Models Understand Molecules?

TL;DR

This study evaluates the ability of large language models, specifically GPT and LLaMA, to understand and generate molecular embeddings from SMILES strings, demonstrating LLaMA's superior performance in molecular property and drug-drug interaction prediction tasks.

Contribution

It compares GPT and LLaMA models for molecular embedding from SMILES, showing LLaMA's effectiveness and potential in cheminformatics applications.

Findings

LLaMA outperforms GPT in molecular property prediction

LLaMA's SMILES embeddings are comparable to pre-trained models

LLaMA surpasses pre-trained models in drug-drug interaction prediction

Abstract

Purpose: Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations. Method: We investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction. Results: We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks. Conclusion: The performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT