# Sequence-based protein-protein interaction prediction and its applications in drug discovery

arXiv: https://arxiv.org/abs/2507.19805 · HTML: https://mlsanity.com/paper/2507.19805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 216 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 15 of 15)

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- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [Deciphering antibody affinity maturation with language models and weakly supervised learning](https://mlsanity.com/paper/2112.07782/) (2021)
- [Learning protein sequence embeddings using information from structure](https://mlsanity.com/paper/1902.08661/) (2019)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [Large scale paired antibody language models](https://mlsanity.com/paper/2403.17889/) (2024)
- [Contemporary AI foundation models increase biological weapons risk](https://mlsanity.com/paper/2506.13798/) (2025)
- [A multitask transfer learning framework for the prediction of virus-human protein-protein interactions](https://mlsanity.com/paper/2111.13346/) (2021)
- [PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs](https://mlsanity.com/paper/2507.05101/) (2025)

## Abstract (arXiv)

Aberrant protein-protein interactions (PPIs) underpin a plethora of human diseases, and disruption of these harmful interactions constitute a compelling treatment avenue. Advances in computational approaches to PPI prediction have closely followed progress in deep learning and natural language processing. In this review, we outline the state-of the-art for sequence-based PPI prediction methods and explore their impact on target identification and drug discovery. We begin with an overview of commonly used training data sources and techniques used to curate these data to enhance the quality of the training set. Subsequently, we survey various PPI predictor types, including traditional similarity-based approaches, and deep learning-based approaches with a particular emphasis on the transformer architecture. Finally, we provide examples of PPI prediction in systems-level proteomics analyses, target identification, and design of therapeutic peptides and antibodies. We also take the opportunity to showcase the potential of PPI-aware drug discovery models in accelerating therapeutic development.