mlsanityData to 7 Oct 2026

Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL

arXiv 2501.12523 ↗First posted 21 Jan 2025cs.LG cs.CRcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Generating unique molecules with biochemically desired properties to serve as viable drug candidates is a difficult task that requires specialized domain expertise. In recent years, diffusion models have shown promising results in accelerating the drug design process through AI-driven molecular generation. However, training these models requires massive amounts of data, which are often isolated in proprietary silos. OpenFL is a federated learning framework that enables privacy-preserving collaborative training across these decentralized data sites. In this work, we present a federated discrete denoising diffusion model that was trained using OpenFL. The federated model achieves comparable performance with a model trained on centralized data when evaluating the uniqueness and validity of the generated molecules. This demonstrates the utility of federated learning in the drug design process. OpenFL is available at: https://github.com/securefederatedai/openfl

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 6 of its 14 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

6 of 14 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022DiGress: Discrete Denoising diffusion for graph generation29 Sep

Other references · 5

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020The Future of Digital Health with Federated Learningcited by 625
  3. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  4. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  5. 2021OpenFL: An open-source framework for Federated Learningcited by 58

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