Skip to content

MolGraphGAN: a graph transformer–adversarial framework for target-specific molecule generation

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.

View source

Similar papers

Preprint Aug 2026

Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks

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...

Daniel Manu, Abee Alazzwi · 0 citations
Jul 2026

CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.

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.

Jianqiang Zheng, Zi-Qi Xu, Junwen Huang et al. · 0 citations
Conference Jul 2026

PR-GVAE: Property-Controllable Molecular Generation via Conditional Graph Variational Autoencoder

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. · 0 citations
Jul 2026

A unified deep generative framework for surrogate-guided molecular discovery across diverse molecular spaces.

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. · 0 citations
Aug 2026

GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.

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.

Jun-Wen He · 0 citations
Open access Aug 2026

Deep Learning-Based Molecular Generation for Lung Cancer Therapeutics

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 · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.