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<p>Molecular generation plays a vital role in advancing drug discovery, materials science, and chemical exploration. In this study, we integrated the conditional variational autoencoder (CVAE) with the Wasserstein generative adversarial network (WGAN) and effectively applied this hybrid architecture to molecular generation tasks, introducing a molecular generation framework with conditional generation capabilities known as CCVAN. The model first encodes the data into a latent vector using CVAE, then shares a decoder with the generator of the WGAN to reconstruct molecular SMILES, and trains the discriminator with both the generated and real molecular SMILES to achieve the goal of conditionally generating molecules. Compared to existing methods, CCVAN can generate molecules with specific properties as needed and performs well in terms of validity and novelty. In a case study, we utilized CCVAN for ligand-based and structure-based drug design, enabling the generation of high-binding-affinity molecules for target binding. Overall, the flexibility and effectiveness of CCVAN make it a valuable tool for accelerating the compound discovery process. The source code of CCVAN is publicly available at <a href="https://github.com/mjcoo/CCVAN">https://github.com/mjcoo/CCVAN</a>.</p> Graphical Abstract <p></p>

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CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Jianqiang Zheng,
  • Ziqi Xu,
  • Junwen Huang,
  • Xiaochuan Chen,
  • Yuetong Li,
  • Yanjie Wei,
  • Huiling Zhang

摘要

Molecular generation plays a vital role in advancing drug discovery, materials science, and chemical exploration. In this study, we integrated the conditional variational autoencoder (CVAE) with the Wasserstein generative adversarial network (WGAN) and effectively applied this hybrid architecture to molecular generation tasks, introducing a molecular generation framework with conditional generation capabilities known as CCVAN. The model first encodes the data into a latent vector using CVAE, then shares a decoder with the generator of the WGAN to reconstruct molecular SMILES, and trains the discriminator with both the generated and real molecular SMILES to achieve the goal of conditionally generating molecules. Compared to existing methods, CCVAN can generate molecules with specific properties as needed and performs well in terms of validity and novelty. In a case study, we utilized CCVAN for ligand-based and structure-based drug design, enabling the generation of high-binding-affinity molecules for target binding. Overall, the flexibility and effectiveness of CCVAN make it a valuable tool for accelerating the compound discovery process. The source code of CCVAN is publicly available at https://github.com/mjcoo/CCVAN.

Graphical Abstract