# Representational Alignment with Chemical Induced Fit for Molecular Relational Learning

arXiv: https://arxiv.org/abs/2502.07027 · HTML: https://mlsanity.com/paper/2502.07027/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2010.11711/) (2020)
- [Towards Scalable Automated Alignment of LLMs: A Survey](https://mlsanity.com/paper/2406.01252/) (2024)
- [Uncovering Neural Scaling Laws in Molecular Representation Learning](https://mlsanity.com/paper/2309.15123/) (2023)
- [Shift-Robust Molecular Relational Learning with Causal Substructure](https://mlsanity.com/paper/2305.18451/) (2023)
- [Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck](https://mlsanity.com/paper/2406.13214/) (2024)
- [Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement](https://mlsanity.com/paper/2407.20499/) (2024)

## Built on by (most cited first, 2 of 2)

- [Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization](https://mlsanity.com/paper/2507.03318/) (2025)
- [Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis](https://mlsanity.com/paper/2605.16799/) (2026)

## Abstract (arXiv)

Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting structural features. The representational similarity between substructure pairs determines the functional compatibility of molecular binding sites. Nevertheless, aligning substructure representations by attention mechanisms lacks guidance from chemical knowledge, resulting in unstable model performance in chemical space (textite.g., functional group, scaffold) shifted data. With theoretical justification, we propose the Representational Alignment with Chemical Induced Fit (ReAlignFit) to enhance the stability of MRL. ReAlignFit dynamically aligns substructure representation in MRL by introducing chemical Induced Fit-based inductive bias. In the induction process, we design the Bias Correction Function based on substructure edge reconstruction to align representations between substructure pairs by simulating chemical conformational changes (dynamic combination of substructures). ReAlignFit further integrates the Subgraph Information Bottleneck during fit process to refine and optimize substructure pairs exhibiting high chemical functional compatibility, leveraging them to generate molecular embeddings. Experimental results on nine datasets demonstrate that ReAlignFit outperforms state-of-the-art models in two tasks and significantly enhances model's stability in both rule-shifted and scaffold-shifted data distributions.