ArticleBMC bioinformatics2024
Can large language models understand molecules?
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- A Multi-Step Computational Workflow for Screening and Prioritizing SHP2-Binding Molecules.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Entropy-based byte patching transformer for self-supervised pretraining of SMILES data.iScience · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Machine learning driven LDFrontiers in oncology · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.Nucleic acids research · 2025Article
- Beyond performance: how design choices shape chemical language models.Journal of cheminformatics · 2025Article
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Article
- PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.Frontiers in pharmacology · 2025Article
- Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeLarge Language Models (LLMs) like Generative Pre-trained Transformer (GPT) 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.
methodWe 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.
resultsWe 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.
conclusionThe 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 .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.