Aug 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 35 references
Medicine
TL;DR
MolGraphGAN provides a generalizable and experimentally usable pipeline for accelerating target-centric drug discovery in a variety of therapeutics, demonstrating scalability across multiple therapeutic classes, and embeds explainable AI protocols through the presentation of attention maps highlighting substructure having maximum influence on predicted binding.
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 netwo...
This study integrated the conditional variational autoencoder with the Wasserstein generative adversarial network and effectively applied this hybrid architecture to molecular generation tasks, introducing a molecular generation framework with conditional generation capabilities known as CCVAN.
Molecular generation is a core task in drug discovery. Although existing deep generative models can produce valid molecular structures, they lack precise control over molecular properties such as lipophilicity (logP) and drug-likeness (QED). This paper proposes Property-Regularized Graph Variational Autoencoder (PR-GVA...
Lu Yu, Tianyu Han, Shuyue Men et al.· 2026 IEEE 27th China Confere...· 0 citations
A high-accuracy surrogate predictor based on message-passing neural networks is decoupled from heterogeneous generators, and integrated into a closed-loop strategy of generate, score, select, and regenerate to progressively enrich high-performing candidates without modifying the underlying model architectures.
Zhao-Sheng Zhang, Yan-Bo Liu, Jia-Dong Liu et al.· Physical Chemistry, Chemical...· 0 citations
Experiments show that GraESM-FuseDTA achieves competitive overall performance and consistent advantages in ranking-oriented and variance-explanation metrics across warm start, drug cold start, target cold start, and strict pair cold start settings.
Despite training on 5000 SMILES due to limited computational resources, the model achieved high validity, strong molecular diversity, and drug-like physicochemical properties, demonstrating the feasibility of a scalable, target-specific AI pipeline for lung cancer drug discovery using fragment-based molecular generatio...
Mohavia Ben Amid Sinon, U. Chude-Okonkwo· Drugs and Drug Candidates· 0 citations
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