The repository for Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey, including related models, datasets/benchmarks, and other resource links.
🔥 We will keep this repository updated.
🌟 If you have a paper or resource you'd like to add, feel free to submit a pull request, open an issue, or email the author at qizhipei@ruc.edu.cn.
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
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SciBERT: A Pretrained Language Model for Scientific Text
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(BlueBERT) Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
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Bio-Megatron: Larger Biomedical Domain Language Model
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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
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BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
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(PubMedBERT) Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
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SciFive: a text-to-text transformer model for biomedical literature
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(DRAGON) Deep Bidirectional Language-Knowledge Graph Pretraining
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LinkBERT: Pretraining Language Models with Document Links
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BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
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BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
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GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
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Large language models encode clinical knowledge
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(ScholarBERT) The Diminishing Returns of Masked Language Models to Science
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PMC-LLaMA: Further Finetuning LLaMA on Medical Papers
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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine
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(GatortronGPT) A study of generative large language model for medical research and healthcare
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Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
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MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
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BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials
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ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
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MedAlpaca - An Open-Source Collection of Medical Conversational AI Models and Training Data
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SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning
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BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
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BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
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Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries
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(MolT5) Translation between Molecules and Natural Language
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(KV-PLM) A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
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(MoMu) A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
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(Text+Chem T5) Unifying Molecular and Textual Representations via Multi-task Language Modelling
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(CLAMP) Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
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GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
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(HI-Mol) Data-Efficient Molecular Generation with Hierarchical Textual Inversion
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MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction
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(ChemLLMBench) What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks
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MolXPT: Wrapping Molecules with Text for Generative Pre-training
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(TextReact) Predictive Chemistry Augmented with Text Retrieval
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MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
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ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction
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(MoleculeSTM) Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
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(AMAN) Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval
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MolLM: A Unified Language Model for Integrating Biomedical Text with 2D and 3D Molecular Representations
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(MolReGPT) Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
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(CaR) Can Large Language Models Empower Molecular Property Prediction?
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MolFM: A Multimodal Molecular Foundation Model
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(ChatMol) Interactive Molecular Discovery with Natural Language
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InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
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ChemCrow: Augmenting large-language models with chemistry tools
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GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
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nach0: Multimodal Natural and Chemical Languages Foundation Model
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DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
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(Ada/Aug-T5) From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
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MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts
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(TGM-DLM) Text-Guided Molecule Generation with Diffusion Language Model
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GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
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PolyNC: a natural and chemical language model for the prediction of unified polymer properties
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MolTC: Towards Molecular Relational Modeling In Language Models
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T-Rex: Text-assisted Retrosynthesis Prediction
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LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
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(Drug-to-indication) Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
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ChemDFM: Dialogue Foundation Model for Chemistry
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DrugAssist: A Large Language Model for Molecule Optimization
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ChemLLM: A Chemical Large Language Model
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(TEDMol) Text-guided Diffusion Model for 3D Molecule Generation
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(3DToMolo) Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
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(ICMA) Large Language Models are In-Context Molecule Learners
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Benchmarking Large Language Models for Molecule Prediction Tasks
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DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMs
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3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs
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(TSMMG) Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
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A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions
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Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
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ReactXT: Understanding Molecular"Reaction-ship"via Reaction-Contextualized Molecule-Text Pretraining
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LDMol: Text-Conditioned Molecule Diffusion Model Leveraging Chemically Informative Latent Space
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(MV-Mol) Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
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HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment
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PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes
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3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular Tokenization
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MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
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DrugLLM: Open Large Language Model for Few-shot Molecule Generation
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(AMOLE) Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models
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Chemical Language Models Have Problems with Chemistry: A Case Study on Molecule Captioning Task
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MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension
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UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
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Chemical Language Model Linker: blending text and molecules with modular adapters
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OntoProtein: Protein Pretraining With Gene Ontology Embedding
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ProTranslator: Zero-Shot Protein Function Prediction Using Textual Description
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ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
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InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
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(ProteinDT) A Text-guided Protein Design Framework
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ProteinChat: Towards Achieving ChatGPT-Like Functionalities on Protein 3D Structures
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Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
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ProtChatGPT: Towards Understanding Proteins with Large Language Models
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ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
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ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
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ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
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ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
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ProteinCLIP: enhancing protein language models with natural language
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ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
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(PAAG) Functional Protein Design with Local Domain Alignment
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(Pinal) Toward De Novo Protein Design from Natural Language
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TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
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Galactica: A Large Language Model for Science
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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
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DARWIN Series: Domain Specific Large Language Models for Natural Science
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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine
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(StructChem) Structured Chemistry Reasoning with Large Language Models
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(BioTranslator) Multilingual translation for zero-shot biomedical classification using BioTranslator
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Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
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(ChatDrug) ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
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BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
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(KEDD) Towards Unified AI Drug Discovery with Multiple Knowledge Modalities
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（Otter Knowledge) Knowledge Enhanced Representation Learning for Drug Discovery
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ChatCell: Facilitating Single-Cell Analysis with Natural Language
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LangCell: Language-Cell Pre-training for Cell Identity Understanding
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BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
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MolBind: Multimodal Alignment of Language, Molecules, and Proteins
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Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
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Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
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An Evaluation of Large Language Models in Bioinformatics Research
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SciMind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences
- A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery Arxiv 2406
- Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule Arxiv 2403
- Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review Arxiv 2403
- From Words to Molecules: A Survey of Large Language Models in Chemistry Arxiv 2402
- Scientific Language Modeling: A Quantitative Review of Large Language Models in Molecular Science Arxiv 2402
- Progress and Opportunities of Foundation Models in Bioinformatics Arxiv 2402
- Scientific Large Language Models: A Survey on Biological & Chemical Domains Arxiv 2401
- The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 Arxiv 2311
- Transformers and Large Language Models for Chemistry and Drug Discovery Arxiv 2310
- Language models in molecular discovery Arxiv 2309
- What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks NeurIPS 2309
- Do Large Language Models Understand Chemistry? A Conversation with ChatGPT JCIM 2303
- A Systematic Survey of Chemical Pre-trained Models IJCAI 2023
- LLM4ScientificDiscovery
- SLM4Mol
- Scientific-LLM-Survey
- Awesome-Bio-Foundation-Models
- Awesome-Molecule-Text
- LLM4Mol
- Awesome-Chemical-Pre-trained-Models
- Awesome-Chemistry-Datasets
- Awesome-Docking
This repository is contributed and updated by QizhiPei and Lijun Wu. If you have questions, don't hesitate to open an issue or ask me via qizhipei@ruc.edu.cn or Lijun Wu via lijun_wu@outlook.com. We are happy to hear from you!
@article{pei2024leveraging,
title={Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey},
author={Pei, Qizhi and Wu, Lijun and Gao, Kaiyuan and Zhu, Jinhua and Wang, Yue and Wang, Zun and Qin, Tao and Yan, Rui},
journal={arXiv preprint arXiv:2403.01528},
year={2024}
}