# Machine learning approaches for interpretable antibody property prediction using structural data

arXiv: https://arxiv.org/abs/2510.23975 · HTML: https://mlsanity.com/paper/2510.23975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 131 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [BERTology Meets Biology: Interpreting Attention in Protein Language Models](https://mlsanity.com/paper/2006.15222/) (2020)
- [Equivariant Graph Neural Networks for 3D Macromolecular Structure](https://mlsanity.com/paper/2106.03843/) (2021)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks](https://mlsanity.com/paper/1502.04381/) (2015)
- [Mapping of attention mechanisms to a generalized Potts model](https://mlsanity.com/paper/2304.07235/) (2023)
- [Graph-based data clustering via multiscale community detection](https://mlsanity.com/paper/1909.04491/) (2019)
- [Geometric graphs from data to aid classification tasks with graph convolutional networks](https://mlsanity.com/paper/2005.04081/) (2020)
- [Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification](https://mlsanity.com/paper/1807.01332/) (2018)
- [Semi-supervised classification on graphs using explicit diffusion dynamics](https://mlsanity.com/paper/1909.11117/) (2019)
- [Sequence-based deep learning antibody design for in silico antibody affinity maturation](https://mlsanity.com/paper/2103.03724/) (2021)

## Abstract (arXiv)

Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine learning (ML) models mostly based on the application of large language models to sequence information have been developed to predict antibody properties. Yet there are open directions to incorporate structural information, not only to enhance prediction but also to offer insights into the underlying molecular mechanisms. This chapter provides an overview of these approaches and describes two ML frameworks that integrate structural data (via graph representations) with neural networks to predict properties of antibodies: ANTIPASTI predicts binding affinity (a global property) whereas INFUSSE predicts residue flexibility (a local property). We survey the principles underpinning these models; the ways in which they encode structural knowledge; and the strategies that can be used to extract biologically relevant statistical signals that can help discover and disentangle molecular determinants of the properties of interest.