AI 中文总结
本文针对GraphGANFed无法按用户定义指标生成分子的问题，提出cGraphGANFed模型，引入评判网络整合评估结果优化生成器，经实验证实其在多指标优化、QED提升及抗非IID数据干扰上表现更优。
AI 中文摘要
生成对抗网络（GAN）因能生成新颖且高质量分子，在分子发现领域备受关注。为高效训练GAN模型同时保护数据隐私，已有研究将联邦学习与图卷积网络融入GAN，提出GraphGANFed模型。但GraphGANFed无法生成仅优化用户定义指标的合成分子，难以助力新药研发。针对该问题，本文对GraphGANFed进行拓展，提出条件图生成对抗网络联邦模型（cGraphGANFed），通过引入评判网络（critic network），利用用户定义指标评估生成分子。评判网络与判别网络的评估结果被整合至生成器的损失函数中，引导生成器生成既保持与真实分子相似化学性质，又能优化用户定义指标的新颖分子。本文在两种场景下开展大量仿真实验：其一，cGraphGANFed尝试优化全部7种常用指标，结果显示在不同设置下，cGraphGANFed在有效性（Validity）和LogP指标上显著优于GraphGANFed，在QED指标上略有优势；其二，cGraphGANFed仅聚焦优化QED指标，结果表明其生成的合成分子QED指标较GraphGANFed提升超10%。此外，实验还证明cGraphGANFed对非独立同分布（non-IID）数据引发的模式崩溃及性能下降具备更强的抵御能力。
英文摘要
Generative adversarial networks (GANs) have garnered considerable attention in molecular discovery for their ability to generate novel and high-quality molecules. To efficiently train a GAN model while preserving data privacy, GraphGANFed has been proposed to incorporate federated learning and graph convolutional networks into GAN. Yet, GraphGANFed cannot produce synthetic molecules that only optimize a user-defined metric(s) to facilitate the new drug discovery process. To address this issue, we introduce a novel extension to GraphGANFed, namely conditional GraphGANFed (cGraphGANFed), by incorporating the critic network to assess generated molecules using user-defined metric(s). The evaluation results from both the critic network and discriminator are integrated into the loss function of the generator, guiding it to generate novel molecules that maintain similar chemical properties to real ones while optimizing user-defined metrics. Extensive simulations are conducted in two scenarios. First, cGraphGANFed endeavors to optimize all seven commonly used metrics, and the results show that cGraphGANFed significantly outperforms GraphGANFed in Validity and LogP, with a slight advantage in QED, across different settings. Second, cGraphGANFed focuses solely on optimizing QED, and the results show that the synthetic molecules produced by cGraphGANFed can achieve more than 10% improvement in QED than GraphGANFed. Also, the results demonstrate cGraphGANFed has enhanced resilience against mode collapses and performance reduction caused by non-IID data.
Comments10 pages, 3 figures
Journal refTCBB-2024-05-0333
