# Learning to design protein-protein interactions with enhanced generalization

arXiv: https://arxiv.org/abs/2310.18515 · HTML: https://mlsanity.com/paper/2310.18515/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 77 references that are in the map.
- First posted on arXiv 27 Oct 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs](https://mlsanity.com/paper/2206.11990/) (2022)
- [SE(3) diffusion model with application to protein backbone generation](https://mlsanity.com/paper/2302.02277/) (2023)
- [EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations](https://mlsanity.com/paper/2306.12059/) (2023)
- [Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking](https://mlsanity.com/paper/2111.07786/) (2021)
- [Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval](https://mlsanity.com/paper/2205.13760/) (2022)
- [End-to-End Learning on 3D Protein Structure for Interface Prediction](https://mlsanity.com/paper/1807.01297/) (2018)
- [DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models](https://mlsanity.com/paper/2304.03889/) (2023)
- [Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity](https://mlsanity.com/paper/2008.12473/) (2020)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction](https://mlsanity.com/paper/2106.04362/) (2021)

## Built on by (most cited first, 16 of 16)

- [Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design](https://mlsanity.com/paper/2505.07086/) (2025)
- [ReactZyme: A Benchmark for Enzyme-Reaction Prediction](https://mlsanity.com/paper/2408.13659/) (2024)
- [Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation](https://mlsanity.com/paper/2503.17361/) (2025)
- [Effective Protein-Protein Interaction Exploration with PPIretrieval](https://mlsanity.com/paper/2402.03675/) (2024)
- [Revealing data leakage in protein interaction benchmarks](https://mlsanity.com/paper/2404.10457/) (2024)
- [Multi-level Interaction Modeling for Protein Mutational Effect Prediction](https://mlsanity.com/paper/2405.17802/) (2024)
- [AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance](https://mlsanity.com/paper/2510.00352/) (2025)
- [Predicting mutational effects on protein binding from folding energy](https://mlsanity.com/paper/2507.05502/) (2025)
- [Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling](https://mlsanity.com/paper/2601.06214/) (2026)
- [Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction](https://mlsanity.com/paper/2505.20589/) (2025)
- [One protein is all you need](https://mlsanity.com/paper/2411.02109/) (2024)
- [Minimal-Action Discrete Schr"odinger Bridge Matching for Peptide Sequence Design](https://mlsanity.com/paper/2601.22408/) (2026)
- [Atom-level Protein Representation Learning Improves Protein Structure Prediction](https://mlsanity.com/paper/2605.22133/) (2026)
- [CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps](https://mlsanity.com/paper/2606.00955/) (2026)
- [TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology](https://mlsanity.com/paper/2607.08803/) (2026)
- [PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning](https://mlsanity.com/paper/2610.04850/) (2026)

## Abstract (arXiv)

Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.