# ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction

arXiv: https://arxiv.org/abs/2405.06649 · HTML: https://mlsanity.com/paper/2405.06649/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [The Impact of Reasoning Step Length on Large Language Models](https://mlsanity.com/paper/2401.04925/) (2024)
- [Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models](https://mlsanity.com/paper/2306.08018/) (2023)
- [Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction](https://mlsanity.com/paper/2105.06709/) (2021)
- [ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training](https://mlsanity.com/paper/2403.07920/) (2024)
- [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://mlsanity.com/paper/2402.00746/) (2024)
- [SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2305.08316/) (2023)
- [Why only Micro-F1? Class Weighting of Measures for Relation Classification](https://mlsanity.com/paper/2205.09460/) (2022)
- [NPHardEval4V: Dynamic Evaluation of Large Vision-Language Models with Effects of Vision](https://mlsanity.com/paper/2403.01777/) (2024)

## Built on by (most cited first, 12 of 12)

- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding](https://mlsanity.com/paper/2502.01563/) (2025)
- [LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models](https://mlsanity.com/paper/2503.03313/) (2025)
- [LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation](https://mlsanity.com/paper/2410.15828/) (2024)
- [Computational Protein Science in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2501.10282/) (2025)
- [Proteo-R1: Reasoning Foundation Models for De Novo Protein Design](https://mlsanity.com/paper/2605.02937/) (2026)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning](https://mlsanity.com/paper/2511.08024/) (2025)
- [PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning](https://mlsanity.com/paper/2508.14765/) (2025)
- [Recent advances in deep learning and language models for studying the microbiome](https://mlsanity.com/paper/2409.10579/) (2024)
- [The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences](https://mlsanity.com/paper/2509.11295/) (2025)
- [Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery](https://mlsanity.com/paper/2605.21522/) (2026)

## Abstract (arXiv)

The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.