Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network.

ABSTRACT: Generative artificial intelligence has the potential to open new vast chemical search spaces, yet existing reinforcement-guided generative adversarial networks (GANs) struggle to produce non-fragmented and property-oriented molecules at scale without compromising other properties. To overcome these limitations, we present Policy-Optimised Wasserstein GAN (POWGAN), a graph-based generator that incorporates a dynamically scaled reward into adversarial training. The scaling factor increases when progress stalls, keeping gradients informative while steadily steering the generator towards user-defined objectives. When POWGAN replaces the loss function in a previous MedGAN architecture, using graph connectivity (non-fragmentation) as the target property, attains 1.00 fully connected quinoline-li

SUBMITTER: Macedo B 

PROVIDER: S-EPMC12777251 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature