Molecular Generative Adversarial Network with Multi-Property
  Optimization
Huidong Tang Chen Li Sayaka Kamei Yoshihiro Yamanishi Yasuhiko Morimoto
Deep generative models, such as generative adversarial networks (GANs), have
been employed for $de~novo$ molecular generation in drug discovery. Most prior
studies have utilized reinforcement learning (RL) algorithms, particularly
Monte Carlo tree search (MCTS), to handle the discrete nature of molecular
representations in GANs. However, due to the inherent instability in training
GANs and RL models, along with the high computational cost associated with MCTS
sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To
tackle these challenges, this study introduces a novel GAN based on
actor-critic RL with instant and global rewards, called InstGAN, to generate
molecules at the token-level with multi-property optimization. Furthermore,
maximized information entropy is leveraged to alleviate the mode collapse. The
experimental results demonstrate that InstGAN outperforms other baselines,
achieves comparable performance to state-of-the-art models, and efficiently
generates molecules with multi-property optimization. The source code will be
released upon acceptance of the paper.