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<p>The generation of molecules with desirable chemical properties presents a critical challenge in fields such as chemical synthesis and drug discovery. Recent advancements in artificial intelligence (AI) and deep learning have significantly contributed to data-driven molecular generation. However, challenges persist due to the inherent sensitivity of simplified molecular input line entry system (SMILES) representations and the difficulty in applying generative adversarial networks (GANs) to discrete data. This study introduces reinforcement learning-driven molecular generative adversarial network (RL-MolGAN), a novel transformer-based discrete GAN framework designed to address these challenges. Unlike traditional transformer architectures, RL-MolGAN uses a first-decoder-then-encoder structure, facilitating the generation of drug-like molecules from both <i>de novo</i> and scaffold-based designs. In addition, RL-MolGAN integrates reinforcement learning (RL) and Monte Carlo tree search (MCTS) techniques to increase the stability of GAN training and optimize the chemical properties of the generated molecules. To further improve the model’s performance, RL-MolWGAN, an extension of RL-MolGAN, incorporates the Wasserstein distance and minibatch discrimination, which together enhance the stability of the GAN. Experimental results from two widely used molecular datasets, namely, QM9 and ZINC, validate the effectiveness of our models in generating high-quality molecular structures with diverse and desirable chemical properties. Our source code is publicly available at: <a href="https://github.com/tang777777/MIR">https://github.com/tang777777/MIR</a>.</p>

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A Reinforcement Learning-driven Transformer GAN for Molecular Generation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Chen Li,
  • Huidong Tang,
  • Ye Zhu,
  • Yoshihiro Yamanishi

摘要

The generation of molecules with desirable chemical properties presents a critical challenge in fields such as chemical synthesis and drug discovery. Recent advancements in artificial intelligence (AI) and deep learning have significantly contributed to data-driven molecular generation. However, challenges persist due to the inherent sensitivity of simplified molecular input line entry system (SMILES) representations and the difficulty in applying generative adversarial networks (GANs) to discrete data. This study introduces reinforcement learning-driven molecular generative adversarial network (RL-MolGAN), a novel transformer-based discrete GAN framework designed to address these challenges. Unlike traditional transformer architectures, RL-MolGAN uses a first-decoder-then-encoder structure, facilitating the generation of drug-like molecules from both de novo and scaffold-based designs. In addition, RL-MolGAN integrates reinforcement learning (RL) and Monte Carlo tree search (MCTS) techniques to increase the stability of GAN training and optimize the chemical properties of the generated molecules. To further improve the model’s performance, RL-MolWGAN, an extension of RL-MolGAN, incorporates the Wasserstein distance and minibatch discrimination, which together enhance the stability of the GAN. Experimental results from two widely used molecular datasets, namely, QM9 and ZINC, validate the effectiveness of our models in generating high-quality molecular structures with diverse and desirable chemical properties. Our source code is publicly available at: https://github.com/tang777777/MIR.