用联邦学习在不共享数据下生成有效分子，助力药物研发
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL
- 基于OpenFL框架训练分布式离散去噪扩散模型
- 生成分子的唯一性和有效性接近集中式训练模型
- 适合需保护数据隐私的医药公司或研究机构
生成具有生物化学所需特性的独特分子以作为潜在药物候选物是一项艰巨任务，需要专业的领域知识。近年来，扩散模型在通过人工智能驱动的分子生成加速药物设计方面展现出良好前景。然而，训练这些模型需要大量数据，而这些数据通常被封闭在专有数据孤岛中。OpenFL是一个联邦学习框架，可在这些分散的数据站点间实现隐私保护的协同训练。本文提出一种基于OpenFL的联邦离散去噪扩散模型。该模型在评估生成分子的唯一性和有效性时，表现与集中式数据训练的模型相当，证明了联邦学习在药物设计中的实用性。OpenFL开源地址：https://github.com/securefederatedai/openfl
原文摘要 · Abstract (English)
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
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