Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL
This work addresses the challenge of privacy-preserving molecular generation for drug design, though it is incremental as it applies an existing federated learning framework to a known diffusion model approach.
The paper tackles the problem of generating unique molecules with desired properties for drug design by proposing a federated discrete denoising diffusion model trained using OpenFL, achieving comparable performance to centralized training in terms of uniqueness and validity of generated molecules.
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