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
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
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 hav...
Abdelrahman H. Hussein, Vikram V. Patel, Sudeep Varshney et al.· Journal of Computer-Aided Mo...· 0 citations
This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.
Kehan Guo, Yili Shen, Jeeyhun Hwang et al.· Proceedings of the 32nd ACM...· 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
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
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