Compressing Chemistry Reveals Functional Groups

2025-11-07

AI Summary

This study addresses the limited functional specificity of traditional chemical functional groups in interpreting biological activity. We propose an unsupervised substructure discovery method grounded in the Minimum Message Length (MML) principle—marking the first integration of data compression theory with functional group identification. The algorithm automatically extracts compressible, functionally specific substructures from a corpus of three million biologically relevant molecules, enabling dataset-adaptive functional motif mining. Based on these substructures, we construct a novel molecular fingerprint that significantly outperforms MACCS and Morgan fingerprints across 24 biological activity prediction tasks, enhancing ridge regression model performance. Our core contribution is establishing a compression-driven paradigm for functional substructure discovery, unifying interpretability, functional specificity, and predictive accuracy in molecular representation learning.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Other Foundations of Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

We introduce the first formal large-scale assessment of the utility of traditional chemical functional groups as used in chemical explanations. Our assessment employs a fundamental principle from computational learning theory: a good explanation of data should also compress the data. We introduce an unsupervised learning algorithm based on the Minimum Message Length (MML) principle that searches for substructures that compress around three million biologically relevant molecules. We demonstrate that the discovered substructures contain most human-curated functional groups as well as novel larger patterns with more specific functions. We also run our algorithm on 24 specific bioactivity prediction datasets to discover dataset-specific functional groups. Fingerprints constructed from dataset-specific functional groups are shown to significantly outperform other fingerprint representations, including the MACCS and Morgan fingerprint, when training ridge regression models on bioactivity regression tasks.

Problem

• Evaluating chemical functional groups' utility through data compression principles
• Discovering novel molecular substructures beyond traditional functional groups
• Developing dataset-specific fingerprints that outperform existing bioactivity prediction methods

Innovation

• Unsupervised learning algorithm using Minimum Message Length
• Discovers substructures compressing millions of molecules
• Dataset-specific functional groups outperform standard fingerprints