Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data

2025-10-22

AI Summary

In federated drug discovery, while federated learning (FL) enables privacy-preserving collaborative modeling across institutions, it remains challenging to perform data-centric tasks—such as diversity assessment and chemical space characterization—without accessing raw molecular data. To address this gap, we propose the first privacy-preserving framework for diversity analysis of federated molecular datasets. Our approach introduces SF-ICF, a chemically aware diversity metric integrating local interpretability analysis; systematically unifies federated clustering methods—including Fed-kMeans, Fed-PCA+Fed-kMeans, and Fed-LSH; and establishes a dual-perspective evaluation system grounded in both mathematical rigor and chemical relevance. Extensive experiments across eight benchmark molecular datasets demonstrate that our framework significantly improves cross-institutional identification of chemical space structure and characterization of data distribution—all without exposing raw data. This work establishes an interpretable, reproducible paradigm for data quality assessment in federated drug discovery.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Security and Privacy: Data transparency and provenance

Abstract

AI methods are increasingly shaping pharmaceutical drug discovery. However, their translation to industrial applications remains limited due to their reliance on public datasets, lacking scale and diversity of proprietary pharmaceutical data. Federated learning (FL) offers a promising approach to integrate private data into privacy-preserving, collaborative model training across data silos. This federated data access complicates important data-centric tasks such as estimating dataset diversity, performing informed data splits, and understanding the structure of the combined chemical space. To address this gap, we investigate how well federated clustering methods can disentangle and represent distributed molecular data. We benchmark three approaches, Federated kMeans (Fed-kMeans), Federated Principal Component Analysis combined with Fed-kMeans (Fed-PCA+Fed-kMeans), and Federated Locality-Sensitive Hashing (Fed-LSH), against their centralized counterparts on eight diverse molecular datasets. Our evaluation utilizes both, standard mathematical and a chemistry-informed evaluation metrics, SF-ICF, that we introduce in this work. The large-scale benchmarking combined with an in-depth explainability analysis shows the importance of incorporating domain knowledge through chemistry-informed metrics, and on-client explainability analyses for federated diversity analysis on molecular data.

Problem

• Analyzing molecular data diversity across distributed private datasets without sharing data
• Evaluating federated clustering methods for representing distributed molecular data structures
• Developing chemistry-informed metrics for federated diversity analysis of molecular datasets

Innovation

• Federated clustering methods analyze distributed molecular data
• Benchmarking three federated approaches against centralized counterparts
• Introducing chemistry-informed metrics for domain-specific evaluation